LS3MIP (v1.0) contribution to CMIP6: the Land Surface, Snow and Soil moisture Model Intercomparison Project - aims, setup and expected outcome

Abstract

The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth system models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems). <br><br> The experiments are subdivided in two components, the first addressing systematic land biases in offline mode ("LMIP", building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework ("LFMIP", building upon the GLACE-CMIP blueprint).

Geosci. Model Dev., 9, 28092832, 2016 www.geosci-model-dev.net/9/2809/2016/ doi:10.5194/gmd-9-2809-2016 Author(s) 2016. CC Attribution 3.0 License.

Bart van den Hurk1, Hyungjun Kim2, Gerhard Krinner3, Sonia I. Seneviratne4, Chris Derksen5, Taikan Oki2, Herv Douville6, Jeanne Colin6, Agns Ducharne24, Frederique Cheruy7, Nicholas Viovy8, Michael J. Puma9, Yoshihide Wada10, Weiping Li11, Binghao Jia12, Andrea Alessandri13, Dave M. Lawrence14, Graham P. Weedon15, Richard Ellis16, Stefan Hagemann17, Jiafu Mao18, Mark G. Flanner19, Matteo Zampieri20, Stefano Materia20, Rachel M. Law21, and Justin Shefeld22,23

1KNMI, De Bilt, the Netherlands

2Institute of Industrial Science, the University of Tokyo, Tokyo, Japan

3LGGE, CNRS, Grenoble, France

4Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

5Climate Research Division, Environment and Climate Change, Toronto, Canada

6CNRM, Centre National de Recherches Mtorologiques, Mto-France, Toulouse, France

7LMD-IPSL, Centre National de la Recherche Scientique, Universit Pierre et Marie-Curie, Ecole Normale Suprieure, Ecole Polytechnique, Paris, France

8LSCE-IPSL: CEA-CNRS-UVSQ, Gif-sur-Yvette, France

9NASA Goddard Institute for Space Studies and Center for Climate Systems Research, Columbia University, New York, USA

10International Institute for Applied Systems Analysis, Laxenburg, Austria

11Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing, China

12State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

13Agenzia Nazionale per le nuove Tecnologie, lenergia e lo sviluppo economico sostenibile, Rome, Italy

14Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, USA

15Met Ofce (JCHMR) Maclean Building Crowmarsh Gifford Wallingford, Oxfordshire, UK

16Centre for Ecology and Hydrology, Maclean Building Crowmarsh Gifford Wallingford, Oxfordshire, UK

17Max-Planck-Institut fr Meteorologie, Hamburg, Germany

18Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

19Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, USA

20Euro-Mediterranean Center for Climate Change (CMCC), Climate Simulation and Prediction Division, Bologna, Italy

21CSIRO Oceans and Atmosphere, Aspendale, Australia

22Department of Civil and Environmental Engineering Princeton University, Princeton, USA

23Geography and Environment, University of Southampton, Southampton, UK

24Sorbonne Universits, UMR 7619 METIS, UPMC/CNRS/EPHE, Paris, France Correspondence to: Bart van den Hurk ([email protected])

Received: 30 March 2016 Published in Geosci. Model Dev. Discuss.: 11 April 2016 Revised: 27 July 2016 Accepted: 28 July 2016 Published: 24 August 2016

Published by Copernicus Publications on behalf of the European Geosciences Union.

LS3MIP (v1.0) contribution to CMIP6: the Land Surface, Snow and Soil moisture Model Intercomparison Project aims, setup and expected outcome

2810 B. van den Hurk et al.: LS3MIP (v1.0) contribution to CMIP6

Abstract. The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth system models (ESMs). The solid and liquid water stored at the land surface has a large inuence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and ux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems).

The experiments are subdivided in two components, the rst addressing systematic land biases in ofine mode (LMIP, building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework (LFMIP, building upon the GLACE-CMIP blueprint).

1 Introduction

Land surface processes, including heat uxes, snow, soil moisture, vegetation, turbulent transfer and runoff, continue to be ranked highly on the list of the most relevant yet complex and poorly represented features in state-of-the-art climate models. People live on land, exploit its water and natural resources and experience day-to-day weather that is strongly affected by feedbacks with the land surface. The six Grand Challenges of the World Climate Research Program (WCRP)1 include topics governed primarily (Water Availability, Cryosphere) or largely (Climate Extremes) by land surface characteristics.

Despite the importance of a credible representation of land surface processes in Earth system models (ESMs), a number of systematic biases and uncertainties persist. Biases in hydrological characteristics (e.g., moisture storage in soil and snow, runoff, vegetation and surface water bodies), partitioning of energy and water uxes (Seneviratne et al., 2010), definition of initial and boundary conditions at the appropriate spatial scale, feedback strengths (Koster et al., 2004; Qu and Hall, 2014) and inherent land surface related predictability

1http://www.wcrp-climate.org/grand-challenges

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(Douville et al., 2007; Dirmeyer et al., 2013) are still subjects of considerable research effort.

These biases and uncertainties are problematic, because they affect, among others, forecast skill (Koster et al., 2010a), regional climate change patterns (Campoy et al., 2013;Seneviratne et al., 2013; Koven et al., 2012) and explicable trends in water resources (Lehning, 2013). In addition, there is evidence of the presence of large-scale systematic biases in some aspects of land hydrology in current climate models (Mueller and Seneviratne, 2014) and the terrestrial component of the carbon cycle (Anav et al., 2013; Mystakidis et al., 2016). Notably, land surface processes can be an important reason for a direct link between the climate models temperature biases in the present period and in the future projections with increased radiative forcings at the regional scale (Cattiaux et al., 2013).

For snow cover, a better understanding of the links with climate is critical for interpretation of the observed dramatic reduction in springtime snow cover over recent decades (e.g., Derksen and Brown, 2012; Brutel-Vuilmet et al., 2013), to improve the seasonal to interannual forecast skill of temperature, runoff and soil moisture (e.g., Thomas et al., 2016; Peings et al., 2011) and to adequately represent polar warming amplication in the Arctic (e.g., Holland and Bitz, 2003).Snow-related biases in climate models may arise from the snow-albedo feedback (Qu and Hall, 2014; Thackeray et al., 2015), but also from the energy sink induced by snow melting in spring and the thermal insulation effect of snow on the underlying soil (Koven et al., 2012; Gouttevin et al., 2012).Temporal dynamics of snowatmospheric coupling during various phases of snow depletion (Xu and Dirmeyer, 2011, 2012) are crucial for a proper representation of the timing and atmospheric response to snow melt. Phase 1 and 2 of the Snow Model Intercomparison Project (SnowMIP) (Etchevers et al., 2004; Essery et al., 2009) provided useful insights in the capacity of snow models of different complexity to simulate the snowpack evolution from local meteorological forcing but did not explore snowclimate interactions. Because of strong snow/atmosphere interactions, it remains difcult to distinguish and quantify the various potential causes for disagreement between observed and modeled snow trends and the related climate feedbacks.

Soil moisture plays a central role in the coupled land vegetationsnowwateratmosphere system (Seneviratne et al., 2010; van den Hurk et al., 2011), where interactions are evident at many relevant timescales: diurnal cycles of land surface uxes, seasonal and subseasonal predictability of droughts, oods and hot extremes, annual cycles governing the water buffer in dry seasons and shifts in the climatology in response to changing patterns of precipitation and evaporation. The representation of historical variations in land water availability and droughts still suffer from large uncertainties, due to model parameterizations, unrepresented hydrologic processes such as lateral groundwater ow, lateral ows connected to re-inltration of river water or irrigation

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with river water, and/or atmospheric forcings (Shefeld et al., 2012; Zampieri et al., 2012; Trenberth et al., 2014; Greve et al., 2014; Clark et al., 2015). This also applies to the energy and carbon exchanges between the land and the atmosphere (e.g., Mueller and Seneviratne, 2014; Friedlingstein et al., 2013).

It is difcult to generate reliable observations of soil moisture and land surface uxes that can be used as boundary conditions for modeling and predictability studies. Satellite retrievals, in situ observations, ofine model experiments (Second Global Soil Wetness Project, GSWP2; Dirmeyer et al., 2006) and indirect estimates all have a potential to generate relevant information but are largely inconsistent, covering different model components, and suffer from methodological aws (Mueller et al., 2013; Mao et al., 2015). As a consequence, the pioneering work on deriving soil moisture related landatmosphere coupling strength (Koster et al., 2004) and regional/global climate responses in both present and future climate (Seneviratne et al., 2006, 2013) has been carried out using (ensembles of) modeling experiments. The second Global Land Atmosphere Coupling Experiment (GLACE2;Koster et al., 2010a) measured the actual temperature and precipitation skill improvement of using GSWP2 soil moisture initializations, which is much lower than suggested by the coupling strength diagnostics. Limited quality of the initial states, limited predictability and poor representation of essential processes determining the propagation of information through the hydrological cycle in the models all play a role.

Altogether, there are substantial challenges concerning both the representation of land surface processes in current-generation ESMs and the understanding of related climate feedbacks. The Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP) is designed to allow the climate modeling community to make substantial progress in addressing these challenges. It is part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6; Eyring et al., 2016). The following section further develops the objectives and rationale of LS3MIP. The experimental design and analysis plan is presented thereafter. The nal discussion section describes the expected outcome and impact of LS3MIP.

2 Objectives and rationale

The goal of the collection of LS3MIP experiments is to provide a comprehensive assessment of land surface, snow and soil moistureclimate feedbacks, and to diagnose systematic biases and process-level deciencies in the land modules of current ESMs. While vegetation, carbon cycle, soil moisture, snow, surface energy balance and landatmosphere interaction are all intimately coupled in the real world, LS3MIP focuses necessarily on the physical land surface in this complex system: interactions with vegetation and carbon cycle are included in the analyses wherever possible without

losing this essential focus. In the complementary experiment Land Use MIP (LUMIP; see Lawrence et al., 2016) and C4MIP (Jones et al., 2016) vegetation, the terrestrial carbon cycle and land management are the central topics of analysis. LS3MIP and LUMIP share some model experiments and analyses (see below) to allow to be addressed the complex interactions at the land surface and yet remain able to focus on well-posed hypotheses and research approaches.

LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, of particular interest for highly vulnerable regions (including densely populated regions, the Arctic, agricultural areas, and some terrestrial ecosystems).

The LS3MIP experiments collectively address the following objectives:

evaluate the current state of land processes including surface uxes, snow cover and soil moisture representation in CMIP DECK (Diagnostic, Evaluation and Characterization of Klima) experiments and CMIP6 historical simulations (Eyring et al., 2016), to identify the main systematic biases and their dependencies;

estimate multi-model long-term terrestrial energy/water/carbon cycles, using the land modules of CMIP6 models under observation-constrained historical (land reanalysis) and projected future (impact assessment) climatic conditions considering land use/land cover changes;

assess the role of snow and soil moisture feedbacks in the regional response to altered climate forcings, focusing on controls of climate extremes, water availability and high-latitude climate in historical and future scenario runs;

assess the contribution of land surface processes to systematic Earth system model biases and the current and future predictability of regional temperature/precipitation patterns.

These objectives address each of the three CMIP6 overarching questions: (1) What are regional feedbacks and responses to climate change?; (2) What are the systematic biases in the current climate models?; and (3) What are the perspectives concerning the generation of predictions and scenarios?

LS3MIP encompasses a family of model experiments building on earlier multi-model experiments, particularly(a) ofine land surface experiments (GSWP2 and its successor GSWP3), (b) the coordinated snow model intercomparisons SnowMIP phase 1 and 2 (Etchevers et al., 2004; Essery et al., 2009), and (c) the coupled climate timescale GLACE-type conguration (GLACE-CMIP, Seneviratne et al., 2013).Within LS3MIP the Land-only experimental suite is referred to as LMIP (Land Model Intercomparison Project) with the experiment ID Land, while the coupled suite is labeled as LFMIP (Land Feedback MIP). A detailed description of the

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2812 B. van den Hurk et al.: LS3MIP (v1.0) contribution to CMIP6

model design is given below, and a graphical display of the various components within LS3MIP is shown in Fig. 1.

As illustrated in Fig. 2, LS3MIP is addressing multiple WCRP Grand Challenges and core projects. The LMIP experiment will provide better estimates of historical changes in snow and soil moisture at global scale, thus allowing the evaluation of changes in freshwater, agricultural drought and streamow extremes over continents and a better understanding of the main drivers of these changes. The LFMIP experiments are of high relevance for the assessment of key feedbacks and systematic biases of land surface processes in coupled mode (Dirmeyer et al., 2015), and are particularly focusing on two of the main feedback loops over land: the snow-albedotemperature feedback involved in Arctic Amplication, and the soil moisturetemperature feedback leading to major changes in temperature extremes (Douville et al., 2016). In addition, LS3MIP will allow the exchange of data and knowledge across the snow and soil moisture research communities that address a common physical topic: terrestrial water in liquid and solid form. Snow and soil moisture dynamics are often interrelated (e.g., Hall et al., 2008;Xu and Dirmeyer, 2012) and jointly contribute to hydrological variability (e.g., Koster et al., 2010b).

LS3MIP will also provide relevant insights for other research communities, such as global reconstructions of land variables that are not directly observed for detection and attribution studies (Douville et al., 2013), estimates of freshwater inputs to the oceans (which are relevant for sea-level changes and regional impacts; Carmack et al., 2015), the assessment of feedbacks shown to strongly modulate regional climate variability relevant for regional climate information, as well as the investigation of land climate feedbacks on large-scale circulation patterns and cloud occurrence (Zampieri and Lionello, 2011). This will thus also imply potential contributions to programmes like the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP; Warszawski et al., 2014) and the International Detection and Attribution Group IDAG.LS3MIP is geared to extend and consolidate available data, models and theories to support human awareness and resilience to highly variable environmental conditions in a large ensemble of sectoral domains, including disaster risk reduction, food security, public safety, nature conservation and societal wellbeing.

Figure 3 illustrates the embedding of LS3MIP within CMIP6. LS3MIP lls a major gap by considering systematic land biases and land feedbacks. In this context, LS3MIP is part of a larger LandMIP series of CMIP6 experiments fully addressing biases, uncertainties, feedbacks and forcings from the land surface (Fig. 1), which are complementary to similar experiments for ocean or atmospheric processes (Seneviratne et al., 2014). In particular, we note that while LS3MIP focuses on systematic biases in land surface processes (Land) and on feedbacks from the land surface processes on the climate system (LFMIP), the complementary Land Use MIP (LUMIP) experiment addresses the role of

Figure 1. Structure of the LandMIPs. LS3MIP includes (1) the ofine representation of land processes (LMIP) and (2) the representation of landatmosphere feedbacks related to snow and soil moisture (LFMIP). Forcing associated with land use is assessed in LUMIP. Substantial links also exist to C4MIP (terrestrial carbon cycle). Furthermore, a land albedo test bed experiment is planned within GeoMIP. From Seneviratne et al. (2014).

land use forcing on the climate system. The role of vegetation and carbon stores in the climate system is a point of convergence between LUMIP, C4MIP and LS3MIP, and the ofine LMIP experiment will serve as land-only reference experiments for both the LS3MIP and LUMIP experiments.In addition, there will also be links to the C4MIP experiment with respect to impacts of snow and soil moisture processes (in particular droughts and oods) on terrestrial carbon exchanges and resulting feedbacks to the climate system.

3 Experimental design

The experimental design of LS3MIP consists of a series of ofine land-only experiments (LMIP) driven by a land surface forcing data set and a variety of coupled model simulations (LFMIP) (see Fig. 4 and Table 1):

3.1 Ofine land model experiments (Land ofine MIP, experiment ID Land)

Ofine simulations of land surface states and uxes allow for the evaluation of trends and variability of snow, soil moisture and land surface uxes, carbon stocks and vegetation dynamics, and climate change impacts. Within the CMIP6 program various Model Intercomparison Projects make use of ofine terrestrial simulations to benchmark or force coupled climate model simulations: LUMIP focusing on the role of land use/land cover change, C4MIP to address the terrestrial component of the carbon cycle and its feedback to cli-

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Figure 2. Relevance of LS3MIP for WCRP Core Projects and Grand Challenges2.

Figure 3. Embedding of LS3MIP within CMIP6. Adapted from Eyring et al. (2015).

mate, and LS3MIP to provide soil moisture and snow boundary conditions.

Meteorological forcings, ancillary data (e.g., land use/cover changes, surface parameters, CO2 concentration and nitrogen deposition) and documented protocols to spin-up and execute the experiments are essential ingredients for a successful ofine land model experiment (Wei et al., 2014). The rst Global Soil Wetness Project (GSWP; Dirmeyer et al., 1999), covering two annual cycles (19871988), established a successful template, which was updated and ne-tuned in a number of follow-up experiments, both with

2http://wcrp-climate.org/index.php/grand-challenges

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Figure 4. Schematic diagram for the experiment structure of LS3MIP. Tier 1 experiments are indicated with a heavy black outline, and complementary ensemble experiments are indicated with white hatched lines. Land-Altforce represents three alternative forcings for the Land-Hist experiment. For further details on the experiments and acronyms, see Table 1 and text.

global (Dirmeyer et al., 2006; Shefeld et al., 2006) and regional (Boone et al., 2009) coverage.

3.1.1 Available data sets for meteorological forcing

Ofine experiments will primarily use GSWP33 (Tier 1) forcing (Kim et al., 2016) with alternate forcing used in Tier 2 experiments.

The third Global Soil Wetness Project (GSWP3) provides meteorological forcings for the entire 20th century and beyond, making extensive use of the 20th Century Reanalysis (20CR) (Compo et al., 2011). In this reanalysis product only surface pressure and monthly sea-surface temperature and sea-ice concentration are assimilated. The ensemble uncertainty in the synoptic variability of 20CR varies with the time-changing observation network. High correlations for

3http://hydro.iis.u-tokyo.ac.jp/GSWP3/

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Table 1. Summary of LS3MIP experiments. Experiments with specic treatment of subsets of land surface features are not listed in this overview.

Experiment ID and tier

Experiment description/ design

Cong (L/A/O)a

Start End No. ensb No. total yearsc

Science question and/or gap being addressed

Synergies with other CMIP6 MIPs

Land-Hist (1) land only simulations

L 1850 2014 1 165 historical land simulations

LUMIP, C4MIP, CMIP6 historical

Land-Hist-cruNcep Land-Hist-princeton Land-Hist-wfdei(2)

land only simulations

L 1901 2014 3 342 as Land-Hist but with three different forcing data sets (Princeton forcing, CRU-NCEP, and WFDEI)

Land-Future (2) land only simulations

L 2015 2100 6 516 climate trend analysis LUMIP, C4MIP,ScenarioMIP

ScenarioMIP

LFMIP-pdLC (1) prescribed land conditions 1980 2014 climate

LAO 1980 2100 1 121 diagnose land-climate feedback including ocean response

ScenarioMIP

LFMIP-pdLC2 (2) as LFMIP-pdLC with multiple model members

LAO 1980 2100 4 484 diagnose land-climate feedback including ocean response

LFMIP-pdLC + SST

(2)

prescribed land conditions 1980 2014 climate; SSTs prescribed

LA 1980 2100 5 605 diagnose land-climate feedback over land

ScenarioMIP

LFMIP-Pobs + SST

(2)

land conditions from Land-Hist; SSTs prescribed

LA 1901 2014 1 115 perfect boundary condition simulations

ScenarioMIP

LFMIP-rmLC (1) prescribedland conditions 30-year running mean

LAO 1980 2100 1 121 diagnose land-climate feedback including ocean response

ScenarioMIP

LFMIP-rmLC2 (2) as LFMIP-rmLC with multiple model members

LAO 1980 2100 4 484 diagnose land-climate feedback including ocean response

LFMIP-rmLC + SST

(2)

prescribedland conditions 30-year running mean; SSTs prescribed

LA 1980 2100 5 605 diagnose land-climate feedback over land

ScenarioMIP

CMIP6 historical

a Cong L/A/O refers to land/atmosphere/ocean model congurations. b No. ens refers to number of ensemble members. c No. total years is total number of simulation years. ptbd experimental protocol needs to be detailed in a later stage.

LFMIP-Pobs (2)ptbd initialized pseudo-observations land

LAO 1980 2014 10 350 land-related seasonal predictability

geopotential height (500 hPa) and air temperature (850 hPa) with an independent long record (19052006) of upper-air data were found (Compo et al., 2011), comparable to forecast skill of a state-of-the-art forecasting system at 3 days lead time.

GSWP3 forcing data are generated based on a dynamical downscaling of 20CR. A simulation of the Global Spectral Model (GSM), run at a T248 resolution ( 50 km) is

nudged to the vertical structures of 20CR zonal and meridional winds and air temperature using a spectral nudging dy-

namical downscaling technique that effectively retains syn-optic features in the higher spatial resolution (Yoshimura and Kanamitsu, 2008). Additional bias corrections using observations, vertical damping (Hong and Chang, 2012) and single ensemble member correction (Yoshimura and Kanamitsu, 2013) are applied, giving considerable improvements.

Weedon et al. (2011) provide the meteorological forcing data for the EU Water and Global Change (WATCH)

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programme4, designed to evaluate global hydrological trends and impacts using ofine modeling. The half-degree resolution, 3-hourly WATCH Forcing Data (WFD) was based on the ECMWF ERA-40 reanalysis and included elevation correction and monthly bias correction using CRU observations (and alternative GPCC precipitation total observations).WATCH hydrological modeling led to the WaterMIP study (Haddeland et al., 2011). The WFD stops in 2001, but within a follow-up project EMBRACE Weedon et al. (2014) generated the WFDEI data set that starts in 1979 and was recently extended to 2014. The WFDEI was based on the WATCH Forcing Data methodology but used the ERA-Interim reanalysis (4D-var and higher spatial resolution than ERA-40) so that there are offsets for some variable in the overlap period with the WFD. The forcing consists of 3-hourly ECMWF ERA-Interim reanalysis data (WFD used ERA-40) interpolated to half degree spatial resolution. The 2 m temperatures are bias-corrected in terms of monthly means and monthly average diurnal temperature range using CRU half degree observations. The 2 m temperature, surface pressure, specic humidity and downwards longwave radiation uxes are sequentially elevation corrected. Shortwave radiation uxes are corrected using CRU cloud cover observations and corrected for the effects of seasonal and interannual changes in aerosol loading. Rainfall and snowfall rates are corrected using CRU wet days per month and according to CRU or GPCC ob-served monthly precipitation gauge totals. The WFDEI data set is also used as forcing to the ISIMIP2.1 project, which focuses on historical validation of global water balance under transient land use change (Warszawski et al., 2014).

To support the Global Carbon Project5 (Le Quere et al., 2009) with annual updates of global carbon pools and uxes, the ofine modeling framework TRENDY6 applies an ensemble of terrestrial carbon allocation and land surface models. For this a forcing data set is prepared in which NCEP reanalysis data are bias corrected using the gridded in situ climate data from the Climate Research Unit (CRU), the so-called CRU-NCEP data set (Viovy and Ciais, 2009). This data set is currently available from 1901 to 2014 at 0.5 horizontal spatial resolution and 6-hourly time step. It is being updated annually.

The Princeton Global Forcing data set7 (Shefeld et al., 2006) was developed as a forcing for land surface and other terrestrial models, and for analyzing changes in near-surface climate. The data set is based on 6-hourly surface climate from the NCEP-NCAR reanalysis, which is corrected for biases at diurnal, daily and monthly timescales using a variety of observational data sets. The data are available at 1.0,0.5 and 0.25 resolution and 3-hourly time step. The latest version (V2.2) covers 19012014, with a real-time extension

4http://www.eu-watch.org/

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5http://www.globalcarbonproject.org/about/index.htm

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6http://dgvm.ceh.ac.uk/node/21

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based on satellite precipitation and weather model analysis elds. The reanalysis precipitation is corrected by adjusting the number of rain days and monthly accumulations to match observations from CRU and the Global Precipitation Climatology Project (GPCP). Precipitation is downscaled in space using statistical relationships based on GPCP and the TRMM Multi-satellite Precipitation Analysis (TMPA), and to 3-hourly resolution based on TMPA. Temperature, humidity, pressure and longwave radiation are downscaled in space with account for elevation. Daily mean temperature and diurnal temperature range are adjusted to match the CRU monthly data. Shortwave and longwave surface radiation are adjusted to match satellite-based observations from the University of Maryland (Zhang et al., 2016) and to be consistent with CRU cloud cover observations outside of the satellite period. An experimental version (V3) assimilates station observations into the background gridded eld to provide local-scale corrections (J. Shefeld, personal communication, February 2016).

Figure 5 shows the performance in terms of correlation and standard deviation of the forcing data sets compared to daily observations from 20 globally distributed in situ FLUXNET sites (Baldocchi et al., 2001). Although for precipitation intrinsic heterogeneity leads to signicant differences with the in situ observations, longwave and shortwave downward radiation (not shown) and air temperature show variability characteristics similar to the observations.

The participating modeling groups are invited to run a number of experiments in this land-only branch of LS3MIP.

3.1.2 Historical ofine simulations: Land-Hist

The Tier 1 experiments of the ofine LMIP experiment consist of simulations using the GSWP3 forcing data for a historical (18312014) interval. The land model conguration should be identical to that used in the DECK and CMIP6 historical simulations for the parent coupled model.

The atmospheric forcing will be prepared at a standard 0[notdef]5 0[notdef]5 spatial resolution at 3-hourly intervals and dis

tributed with a package to regrid data to the native grids of the global climate models (GCMs). Also vegetation, soil, topography and land/sea mask data will be prescribed following the protocol used for the CMIP6 DECK simulations. Spin-up of the land-only simulations should follow the TRENDY protocol8 which calls for recycling of the climate mean and variability from two decades of the forcing data set (e.g., 18311850 for GSWP3, 19011920 for the alternative land surface forcings). Land use should be held constant at 1850 as in the DECK 1850 coupled control simulation (piControl). See discussion and denition of constant land-use in Sect. 2.1 of LUMIP protocol paper (Lawrence et al., 2016).CO2 and all other forcings should be held constant at 1850 levels during spinup. For the period 1850 to the rst year of

8http://dgvm.ceh.ac.uk/node/9

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Figure 5. Taylor diagram for evaluating the forcing data sets comparing to daily observations from FLUXNET sites, as used by (Best et al., 2015): (a) 2 m air temperature and (b) precipitation. Red, blue and green dots indicate GSWP3, Watch Forcing Data (Weedon et al., 2011) and Princeton forcing (Shefeld et al., 2006), respectively. Grey and orange dots indicate 20CR and its dynamically downscaled product (GSM248).

the forcing data set, the forcing data should continue to be recycled but all other forcings (land-use, CO2, etc.) should be as in the CMIP6 historical simulation. Transient land use is a prescribed CMIP6 forcing and is described in the LUMIP protocol (Lawrence et al., 2016).

Interactions with the ocean MIP (OMIP; Grifes et al., 2016) are arranged by the use of terrestrial freshwater uxes produced in the LMIP simulations as a boundary condition for the forced ocean-only simulations in OMIP, in addition to the forcing provided by (Dai and Trenberth 2002).

Single site time series of in situ observational forcing variables from selected reference locations (from FLUXNET, Baldocchi et al., 2001) are supplied in addition to the forcing data for additional site level validation. This allows the evaluation of land surface models in current GCMs such as applied by Best et al. (2015) and in ESM-SnowMIP (Earth System Model Snow Module Intercomparison Project; see below). For snow evaluation, an international network of well-instrumented sites has been identied, covering the major climate classes of seasonal snow, each of which poses unique challenges for the parameterization of snow related processes (see analysis strategy below).

Although Land-Hist is not a formal component of the DECK simulations which form the core of CMIP6 (see Fig. 3), the WCRP Working Group on Climate Modeling (WGCM) recognized the importance of these land-only experiments for the process of model development and benchmarking. A future implementation of a full or subset of this historical run is proposed to become part of the DECK in future CMIP exercises and is included as a Tier 1 experiment in LS3MIP. Land surface model output from this subset of

LMIP will also be used as boundary condition in some of the coupled climate model simulations, described below.

3.1.3 Historical simulations with alternative forcings

Additional Tier 2 experiments are solicited where the experimental setup is similar to the Tier 1 simulations, but using 3 alternative meteorological forcing data sets that differ from GSWP3: the Princeton forcing (Shefeld et al., 2006), WFD and WFDEI combined (allowing for offsets as needed; Weedon et al., 2014) and the CRU-NCEP forcing (Wei et al., 2014) used in TRENDY (Sitch et al., 2015). These Tier 2 experiments cover the period 19012014. The model outputs will allow assessment of the sensitivity of land-only simulations to uncertainties in forcing data. Differences in the outputs compared to the primary runs with the GSWP3 forcing will help in understanding simulation sensitivity to the selection of forcing data sets. Kim (2010) utilized a similarity index ([Omega1]; Koster et al., 2000) to estimate the uncertainty derived from an ensemble of precipitation observation data sets relative to the uncertainty from an ensemble of model simulations for evapotranspiration and runoff. The joint utilization of common monthly observations by the various forcing data sets leads to a high value of [Omega1] when evaluated using monthly mean values. However, evaluation of data set consistency of monthly variance leads to much larger disparities and considerably lower values of [Omega1] (Fig. 6). This uncertainty will propagate differently to other hydrological variables, such as runoff or evapotranspiration (Kim, 2010).

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Figure 6. Global distributions of the similarity index ([Omega1]) for 20012010 of monthly mean (a, c) and (b, d) monthly variance (calculated from daily data from each data set) of 2 m air temperature (top panels) and precipitation (bottom panels), respectively. Shown are global distributions and zonal means. After Kim (2010).

3.1.4 Climate change impact assessment: Land-Future

A set of future land-only time slice simulations (20152100) will be generated via forcing data obtained from at least 2 future climate scenarios from the ScenarioMIP (ONeill et al., 2016) and will be executed at a later stage during CMIP6.Tentatively, Shared Socioeconomic Pathway SSP5-8.5 and SSP4-3.79 will be selected, run by 3 model realizations each.The models will be chosen based on the evaluation of the results from the Historical simulations from the CMIP6 Nucleus in order to represent the ensemble spread efciently and reliably (Evans et al., 2013). To generate a set of ensemble forcing data for the future, a trend preserving statistical bias correction method will be applied to the 3-hourly surface meteorology variables (Table A4) from the scenario output (Hempel et al., 2013; Watanabe et al., 2014). Gridded forcings will be provided in a similar data format as the historical simulations.

Land-Future is a Tier 2 experiment in LS3MIP and focuses on assessment of climate change impact (e.g., shifts of the occurrence of critical water availability due to changing statistical distributions of extreme events) and on the assessment of the land surface analogue of climate sensitivity for various key land variables (Perket et al., 2014; Flanner et al., 2011).

3.2 Prescribed land surface states in coupled models for land surface feedback assessment (Land Feedback MIP, LFMIP)

Land surface processes do not act in isolation in the climate system. A tight coupling with the overlying atmosphere takes place on multiple temporal and spatial scales. A systematic

9https://cmip.ucar.edu/scenario-mip/experimental-protocols

Web End =https://cmip.ucar.edu/scenario-mip/experimental-protocols

assessment of the strength and spatial structure of land surface interaction at subcontinental, seasonal timescales has been performed with the initial GLACE setup (GLACE1 and GLACE2 experiments; Koster et al., 2004) in which essentially the spread in an ensemble simulation of a coupled landatmosphere model was compared to a model congu-ration in which the landatmosphere interaction was greatly bypassed by prescribing soil conditions throughout the simulation in all members of the ensemble. Examination of the signicance of landatmosphere feedbacks at the centennial climate timescale was later explored at the regional scale in a single-model study (Seneviratne et al., 2006) and on global scale in the GLACE-CMIP5 experiment in a small model ensemble (Seneviratne et al., 2013).

A protocol very similar to the design of GLACE-CMIP5 is followed in LFMIP. Parallel to a set of reference simulations taken from the CMIP6 DECK, a set of forced experiments is carried out where land surface states are prescribed from or nudged towards predescribed elds derived from coupled simulations. The land surface states are prescribed or nudged at a daily timescale. This setup is similar to the Flux Anomaly Forced MIP (FAFMIP, Gregory et al., 2016), where the role of oceanatmosphere interaction at climate timescales is diagnosed by idealized surface perturbation experiments.

While earlier experiments used model congurations with prescribed SST and sea ice conditions, the Tier 1 experiment in LFMIP will be based on coupled atmosphereocean global climate model (AOGCM) simulations and comprise simulations for a historical (19802014) and future (20152100) time range. The selection of the future scenario (from the ScenarioMIP experiment) will be based on the choices made in the ofine LMIP experiment (see above).

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In GLACE-CMIP5 only soil moisture states were prescribed in the forced experiments. The conguration of the particular land surface models may introduce the need to make different selections of land surface states to be prescribed, for instance to avoid strong inconsistencies in the case of frozen ground (soil moisture rather than soil water state should be prescribed; M. Hauser, ETH Zurich, personal communication, 2015), melting snow or growing vegetation. Prescribing surface soil moisture only (experiment S in Koster et al., 2006) gave unrealistic values of the surface Bowen ratio. A standardization of this selection is dif-cult as the implementation and consequences may be highly model specic. Here we recommend to prescribe only the water reservoirs (soil moisture, snow mass). The disparity of possible implementations is adding to the uncertainty range generated by the model ensemble, similar to the degree to which implementation of land use, ux corrections or down-scaling adds to this uncertainty range. Participating modeling groups are encouraged to apply various test simulations focusing both on technical feasibility and experimental impact to evaluate different procedures to prescribe land surface conditions.

The earlier experience with GLACE-type experiments has revealed a number of technical and scientic issues. Because in most GCMs the land surface module is an integral part of the code describing the atmosphere, prescribing land surface dynamics requires a non-conventional technical interface, reading and replacing variables throughout the entire simulations. Many LS3MIP participants have participated earlier in GLACE-type experiments, but for some the code adjustments will require a technical effort. Interpretation of the effect of the variety of implementations of prescribed land surface variables by the different modeling groups (see above) is helped by a careful documentation of the way the modeling groups have implemented this interface. Tight coordination and frequent exchange among the participating modeling groups on the technical modalities of the implementation of the required forcing methods will be ensured during the preparatory phase of LS3MIP in order to maximize the coherence of the modeling exercise and to facilitate the interpretation of the results.

By design, the prescribed land surface experiments do not fully conserve water and energy, similar to the setup of the Atmospheric Model Intercomparison Project (AMIP), nudged and data assimilation experiments. A systematic addition or removal of water or energy can even emerge as a result of asymmetric land surface responses to dry and to wet conditions, e.g., when surface evaporation or runoff depend strongly non-linearly to soil moisture or snow states (e.g., Jaeger and Seneviratne, 2011). Also, unrepresented processes (such as water extraction for irrigation or exchange with the groundwater) may lead to imbalances in the budget (Wada et al., 2012). This systematic alteration of the water and energy balance may not only perturb the simulation of present-day climate (e.g., Douville, 2003; Douville et al.,

2016) but may also interact with the projected climate change signal, where altered climatological soil conditions can contribute to the climate change induced temperature or precipitation signal or water imbalances can lead to imposed runoff changes that could affect ocean circulation and SSTs. Earlier GLACE-type experiments revealed that the problems of water conversion are often reduced when prescribed soil water conditions are taken as the median rather than the mean of a sample over which a climatological mean is calculated (Hauser et al., 2016). In the analyses of the experiments this asymmetry and lack of energy/water balance closure will be examined and put in context of the climatological energy and water balance and its climatic trends.

To be able to best quantify the forcing that prescribing the land surface state represents, the increments of both snow and soil moisture imposed as a consequence of this prescription are required as an additional output. This will enable us to estimate the amplitude of implicit water and energy uxes imposed by the forcing procedure.

Complementary experiments following an almost identical setup as LFMIP, but limiting the prescription of land surface variables to snow-related variables and thus leaving soil moisture free-running, are carried out in the framework of the ESM-SnowMIP carried out within the WCRP Grand Challenge Melting Ice and Global Consequences10. ESM

SnowMIP being tightly linked to LS3MIP, these complementary experiments will allow separating effects of soil moisture and snow feedbacks.

3.2.1 Tier 1 experiments in LFMIP

Similar to the setup of GLACE-CMIP5 (Seneviratne et al., 2013), the core experiments of LFMIP (tier 1) evaluate two different sets of prescribed land surface conditions (snow and soil moisture):

LFMIP-pdLC: the experiments comprise transient coupled atmosphereocean simulations in which a selection of land surface characteristics is prescribed rather than interactively calculated in the model. This climatological land surface forcing is calculated as the mean annual cycle in the period 19802014 from the historical GCM simulations. The experiment aims at diagnosing the role of landatmosphere feedback at the climate timescale. Seneviratne et al. (2013) found a substantial effect of changes in climatological soil moisture on projected temperature change in a future climate, both for seasonal mean and daytime extreme temperature in summer. Effects on precipitation are less clear, and the multi-model nature of LS3MIP is designed to sharpen these quantitative effects. Also, LS3MIP will take a potential damping (or amplifying) effect of oceanic responses on altered land surface conditions into account,

10http://www.climate-cryosphere.org/activities/targeted/esm-snowmip

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in contrast to GLACE-CMIP5. Experiments using this setup (i.e., coupled ocean) in a single-model study have shown that the results could be slightly affected by the inclusion of an interactive ocean, although the effects were not found to be large overall (Orth and Seneviratne, 2016).

LFMIP-rmLC: a prescribed climatology using a transient 30-yr running mean, where a comparison to the standard CMIP6 runs allows diagnosis of shifts in the regions of strong landatmosphere coupling as recorded by e.g., Seneviratne et al. (2006), and shifts in potential predictability related to land surface states (Dirmeyer et al., 2013).

Both sets of simulations cover the historical period (1850 2014) and extend to 2100, based on a forcing scenario to be identied at a later stage. The procedure to initialize the land surface states in the ensemble members is left to the participant, but should allow to generate sufcient spread that can be considered representative for the climate system under study Koster et al. (2006) proposed a preference hierarchy of methods depending on the availability of initialization elds, and LS3MIP will follow this proposal.

Output in high temporal resolution (daily, as well as sub-daily for some elds and time slices) is required to address the role of land surfaceclimate feedbacks on climate extremes over land.

Multi-member experiments are encouraged, but the mandatory tier 1 simulations are limited to one realization for each of the two prescribed land surface time series described above.

3.2.2 Tier 2 experiments in LFMIP

To analyze a number of additional features of land atmosphere feedbacks, a collection of tier 2 simulations is proposed in LS3MIP.

Simulations with observed SST The AOGCM simulations from Tier 1 are duplicated with a prescribed SST conguration taken from the AMIP runs in the DECK atmospheric global climate model (AGCM), in order to isolate the role of the ocean in propagating and damping/reinforcing land surface responses on climate (Koster et al., 2000). Both the historic and running mean land surface simulations are requested (LFMIP-pdLC + SST and -rmLC + SST, respectively).

Simulations with observed SST and Land-Hist output A pseudo-observed boundary condition set of experiments use the AMIP SSTs and the Land-Hist land boundary conditions generated by the land surface model used in the participating ESM, leading to simulations driven by surface elds that are strongly controlled by observed forcings. This will only cover the historic period (19012014) (LFMIP-PObs + SST). For this the

land-only simulations in LMIP need to be interpolated to the native GCM grid, preserving landsea boundaries and other characteristics.

Separate effects of soil moisture and snow, and role of additional land parameters and variables Additional experiments, in which only snow, snow albedo or soil moisture is prescribed will be conducted to assess the respective feedbacks in isolation, and have control on possible interactions between snow cover and soil moisture content. Also vegetation parameters and variables (e.g., leaf area index, canopy height and thickness) are considered. These experiments are not listed in Table 1, but will be detailed in a follow-up protocol to be dened later.

Fixed land use conditions In conjunction with the Land Use MIP (LUMIP), a repetition of the Tier 1 experiment under xed 1850 land cover and land use conditions highlights the role of soil moisture in modulating the climate response to land cover and land use (not listed in Table 1).

3.3 Prescribed land surface states derived from pseudo-observations (LFMIP-Pobs)

The use of LMIP (land-only simulations) to initialize the AOGCM experiments (LFMIP) allows a set of predictability experiments in line with the GLACE2 setup (Koster et al., 2010a). The LFMIP-Pobs experiment is an extension to GLACE2 by (a) allowing more models to participate, (b) improving the statistics by extending the original 19861995 record to 19802014, (c) evaluating the quality of newly available land surface forcings and (d) executing the experiments in AOGCM mode. Koster et al. (2010a) and van den Hurk et al. (2012) concluded that the forecast skill improvement from models using initial soil moisture conditions was relatively low. Possible causes for this low skill are the limited record length and limited quality of the (precipitation) observations used to generate the soil conditions. These issues are explicitly addressed in LFMIP-Pobs.

All LFMIP-Pobs experiments are Tier 2, which also gives room for additional model design elements such as the evaluation of various observational data sources (such as for snow mass (Snow Water Equivalent; SWE) or snow albedo, using satellites derived, reanalysis and land surface model outputs).The predictability assessments include the evaluation of the contribution of snow cover melting and its related feedbacks to the underestimation of recent boreal polar warming by climate models.

The experimental protocol (number of simulations years, ensemble size, initialization, model conguration, output diagnostics) has a strong impact on the results of the experiment (e.g., Guo and Dirmeyer, 2013). This careful design of the LFMIP-Pobs experiment needed for a successful implementation has currently not yet taken place. Therefore these

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experiments are listed as Tier 2 in Table 1, with the comment that the detailed experimental protocol still needs to be dened.

4 Analysis strategy

LS3MIP is designed to push the land surface component of climate models, observational data sets and projections to a higher level of maturity. Understanding the propagation of model and forecast errors and the design of model parameterizations is essential to realize this goal. The LS3MIP steering group is a multi-disciplinary team (climate modelers, snow and soil moisture model specialists, experts in local and remotely sensed data of soil moisture and snow properties) that ensures that the experiment setups, model evaluations and analyses/interpretations of the results are pertinent.

For both snow and soil moisture the starting point will be a careful analysis of model results from on the one hand (a) the DECK historic simulations (both the AMIP and the historical coupled simulation) and (b) on the other hand the (ofine)LMIP historical simulations.

For the evaluation of snow representation in the models, large-scale high-quality data sets of snow mass (SWE) and snow cover extent (SCE) with quantitative uncertainty characteristics will be provided by the Satellite Snow Product Intercomparison and Evaluation Experiment (SnowPEX11).

Analysis within SnowPEX is providing the rst evaluation of satellite derived snow extent (15 participating data sets) and SWE derived from satellite measurements, land surface assimilation systems, physical snow models and reanalyses (7 participating data sets). Internal consistency between products, and bias relative to independent reference data sets are being derived based on standardized and consistent protocols. The evaluation of variability and trends in terrestrial snow cover extent and mass was examined previously for CMIP3 and CMIP5 by e.g., Brown and Mote (2009), Derksen and Brown (2012) and Brutel-Vuilmet et al. (2013).While these assessments were based on single observational data sets, and hence provide no perspective on observational uncertainty and spread relative to multi-model ensembles, standardized multi-source data sets generated by SnowPEX will allow assessment using a multi-data-set observational ensemble (e.g., Mudryk et al., 2015). For snow albedo, multiple satellite-derived data sets are available, including 16-day MODIS12 data from 2001present, the ESA GlobAlbedo product13, the recently updated twice-daily APP-x14 product (19822011), and a derivation of the snow shortwave radiative effect from 20012013 (Singh et al., 2015). Satellite retrievals of snow cover fraction in forested and mountainous areas is an ongoing area of uncertainty which inuences the

11http://calvalportal.ceos.org/projects/snowpex

Web End =http://calvalportal.ceos.org/projects/snowpex 12http://modis-atmos.gsfc.nasa.gov/ALBEDO/

Web End =http://modis-atmos.gsfc.nasa.gov/ALBEDO/

13http://www.globalbedo.org

Web End =http://www.globalbedo.org 14http://stratus.ssec.wisc.edu/products/appx/appx.html

Web End =http://stratus.ssec.wisc.edu/products/appx/appx.html

essential diagnostics related to climate sensitivity of snow cover (Thackeray et al., 2015), feeding into essential diagnostics related to climate sensitivity of snow cover (Qu and Hall, 2014; Fletcher et al., 2012).

In the case of soil moisture, land hydrology and vegetation state, several observations-based data sets will be used in the evaluation of the coupled DECK simulations and ofine Land experiments. Data considered will include the rst multidecadal satellite-based global soil moisture record (Essential Climate Variable Soil Moisture ECVSM) (Liu et al., 2012; Dorigo et al., 2012), long-term (20022015) records of terrestrial water storage from the GRACE satellite (Rodell et al., 2009; Reager et al., 2016; Kim et al., 2009), the multi-product LandFlux-EVAL evapotranspiration synthesis (Mueller et al., 2013), multi-decadal satellite retrievals of the Fraction of Photosynthetically Absorbed Radiation (FPAR, e.g., Gobron et al., 2010; Zscheischler et al., 2015), and up-scaled Fluxnet based products (Jung et al., 2010).

Several details of snow and soil moisture dynamical processes can be indirectly inferred through the analysis of river discharge (Orth et al., 2013; Zampieri et al., 2015). Variables simulated by the routing schemes included in the land surface models can be compared with the station data available from the Global Runoff Database (GRDC15). Combined use of in situ discharge observations and terrestrial water storage changes observed by GRACE will verify how the land surface simulations partition the terms in the water balance equation (i.e., precipitation, evapotranspiration, runoff and water storage changes)(Kim et al., 2009).

The coupled LS3MIP (LFMIP) simulations will be analyzed in concert with the control runs to quantify various climatic effects of snow and soil moisture, detect systematic biases and diagnose feedbacks. Anticipated analyses include the following.

Drivers of variability at multiple timescales Comparison of simulations with prescribed soil moisture and snow (LFMIP-pdLC) allows quantication of the impact of land surface state variability on variability of climate variables such as temperature, relative humidity, cloudiness, precipitation and river discharge at several timescales. The LFMIP-rmLC simulation allows evaluation of this contribution on seasonal timescales, and changes of patterns of high/low land surface impact in a future climate. In particular, a focus will be put on impacts on climate extremes (temperature extremes, heavy precipitation events, see e.g., Seneviratne et al., 2013) and the possible role of land-based feedbacks in amplifying regional climate responses compared to changes in global mean temperature (Seneviratne et al., 2016). A secondary focus will be on the impacts of snow and soil moisture variability on the extremes of river discharge, which can be related to large-scale oods and to nonlocal propagation of drought signals. These aspects will

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be analyzed in the context of water management and to quantify feedbacks of river discharge to the climate system (through the discharge in the oceans, Materia et al., 2012; Carmack et al., 2015) and to the carbon cycle (through the methane produced in ooded areas, Meng et al., 2015).

Attribution of model disagreement The multi-model set up of the experiment allows closer inspection of the effects of modeled soil moisture and snow (and related processes such as plant transpiration, photosynthesis, or snowmelt) on calculated land temperature, precipitation, runoff, vegetation state, and gross primary production. The comparison of LFMIP-pdLC and LFMIP-rmLC will be useful to isolate model disagreement in land surface feedbacks potentially induced by including coupling to a dynamic ocean despite similar land response to climate change.

Emergent constraints While the annual cycle of snow cover and local temperature (Qu and Hall, 2014), and the relation between global mean temperature uctuations and CO2-concentration (Cox et al., 2013) provide observational constraints on snow-albedo and carbon climate feedback, respectively, similar emergent constraints may be dened to constrain (regional) soil moisture or snow related feedbacks with temperature or hydrological processes such as, for instance, the timing of spring onset which may be related to snowmelt, spring river discharge (Zampieri et al., 2015) and vegetation phenology (Xu et al., 2013). Use of appropriate observations and diagnostics as emergent constraints will reduce uncertainties in projections of mean climate and extremes (heat extremes, droughts, oods) (Hoffman et al., 2014). The analysis of amplitude and timing of seasonality of hydrological and ecosystem processes will provide additional diagnostics.

Attribution of model bias A positive relationship between model temperature bias in the current climate, and (regional) climate response can partly be attributed to the soil moistureclimate feedback, which acts on both the seasonal and climate timescale (Cheruy et al., 2014). A multi-model assessment of this relationship is enabled via LS3MIP. The comparison of AMIP-DECK, LFMIP-CA and LFMIP-LCA will be used to assess the impact of atmospheric-related errors in land boundary conditions on the AGCM biases.

Changes in feedback hotspots and predictability patterns Land surface conditions dont exert uniform inuence on the atmosphere in all areas of the globe: a distribution of strong interaction hotspots and areas of high potential predictability contributions from the land surface exists (e.g., Koster et al., 2004). These patterns may change in a future climate (e.g., Seneviratne et al., 2006). A multi-model assessment such as

the one foreseen in LS3MIP allows mapping changes in these patterns, with implications for the occurrence of droughts, heat waves, irrigation limitations or river discharge anomalies and their predictability (Dirmeyer et al., 2013).

Snow shortwave radiative effect analysis The snow shortwave radiative effect (SSRE) can be diagnosed through parallel calculations of surface albedo and shortwave uxes with and without model snow on the ground or in the vegetation canopy (Perket et al., 2014).This metric provides a precise, overarching measure of the snow-induced perturbation to solar absorption in each model, integrating over the variable inuences of vegetation masking, snow grain size, snow cover fraction, soot content, etc. SSRE is analogous to the widely used cloud radiative effect diagnostic, and its time evolution provides a measure of snow albedo feedback in the context of changing climate (Flanner et al., 2011).We recommend that the diagnostic snow shortwave radiative effect (SSRE) calculation be implemented in standard LS3MIP simulations (Tiers 1 and 2). This will enable us to evaluate the integrated effect of model snow cover on surface radiative uxes.

Complementary snow-related ofine experiments Additional ofine experiments are enabled by the provision of a collection of localized forcing data in the Land-Hist experiment (see above). For snow, a network of well-equipped sites is analyzed in detail for characteristic features (for example, snowvegetation interactions for taiga snow; wind-driven processes for tundra snow; snowrain partitioning for maritime snow). Reference simulations at these sites, consistent with previous SnowMIP experiments (Essery et al., 2009), will be complemented by additional experiments with (1) a xed snow albedo; and (2) the insulative properties of snow removed in order to isolate the contributions of snow to the surface energy budget and ground thermal regime. This will be implemented within the ESMSnowMIP16 initiative, aimed at improving our understanding of sources of coupled model biases (global ofine and site scale experiments) in order to identify priority avenues for future model development.

Regarding the snow analyses, the initial geographical focus of LS3MIP is on the continental snow cover of both hemispheres, both in ice-free areas (Northern Eurasia and North America) and on the large ice sheets (Greenland and Antarctica). Effects of snow on sea ice and the quality of the representation of snow on sea ice in climate models will be explored later, but they are of interest because of strong recent trends of Arctic sea ice decline and the potential amplifying effect of earlier spring snow melt over land.

16http://www.climate-cryosphere.org/activities/targeted/esm-snowmip

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Table 2. Earth system modeling groups participating in LS3MIP.

Model name Institute Country

ACCESS CSIRO/Bureau of Meteorology AustraliaACME Land Model U.S. Department of Energy USA BCC-CSM2-MR BCC, CMA ChinaCanESM CCCma CanadaCESM USA CMCC-CM2 Centro Euro-Mediterraneo sui Cambiamenti Climatici ItalyCNRM-CM CNRM-CERFACS FranceEC-Earth SMHI and 26 other institutes Sweden and 9 other

European countries FGOALS LASG, IAP, CAS ChinaGISS NASA GISS USAIPSL-CM6 IPSL France MIROC6-CGCM AORI, University of Tokyo/JAMSTEC/National Japan

Institute for Environmental StudiesMPI-ESM Max Planck Institute for Meteorology (MPI-M) Germany MRI-ESM1.x Meteorological Research Institute Japan NorESM Norwegian Climate Service Centre Norway hadGEM3 Met Ofce UK

For soil moisture, the geographical focus is on all land areas, with special interest in agricultural locations with strong landatmosphere interaction (transition zones between wet and dry climates), extensive irrigation areas, and high inter-annual variability of warm season climate in densely populated areas.

The analyses are carried out on a standardized model output data set. A summary of the requested output data is given in tables in the Appendix.

5 Time line, participating models and interaction strategy

The ofine land surface experiments (Land-Hist) are expected to be completed in early 2017. Future time slices can only be performed when the Scenario-MIP results become available. All coupled LS3MIP simulations and their subsequent analyses will be timed after the completion of the DECK and historical 20th century simulations, expected by mid-2017. Table 2 lists the participating Earth system modeling groups.

The organizational structure of LS3MIP relies on active participation of modeling groups. Coordination structures are in place for the collection and dissemination of data and model results (Eyring et al., 2016), and for the organization of meetings and seminars (by the core team members of LS3MIP, rst six authors of this manuscript). Different from earlier experiments such as GSWP2 and GLACE1/2, no central analysis group is put in place that is responsible for the analyses as proposed in this manuscript. The execution and publication of analyses is considered to be a community ef-

fort of participating researchers, in order to avoid duplication of efforts and coordinate the production of scientic papers.

6 Discussion: expected outcome and impact of LS3MIP

The treatment of the land surface in the current generation of climate models plays a critical role in the assessment of potential effects of widespread changes in radiative forcing, land use and biogeochemical cycles. The land surface both receives climatic variations (by its atmospheric forcing) and returns these variations as feedbacks or land surface features that are of high relevance to the people living on it. The strong coupling between land surface, atmosphere, hydrosphere and cryosphere makes an analysis of its performance characteristics challenging: the response and the state of the land surface strongly depend on the climatological context, and metrics of interactions or feedbacks, which are all difcult to dene and observe (van den Hurk et al., 2011).

LS3MIP addresses these challenges by enhancing earlier diagnostic studies and experimental designs. Within the limits to which complex models such as ESMs can be evaluated with currently available observational evidence (see e.g., the interesting philosophical discussion on climate model evaluation by Lenhard and Winsberg, 2010) it will lead to enhanced understanding of the contribution of land surface treatment to overall climate model performance; give inspiration on how to optimize land surface parameterizations or their forcing; support the development of better forecasting tools, where initial conditions affect the trajectory of the forecast and can be used to optimize forecast skill; and, last but not least, provide a better historical picture of the evolution

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B. van den Hurk et al.: LS3MIP (v1.0) contribution to CMIP6 2823

of our vital water resources during the recent century. In particular, LS3MIP will provide a solid benchmark for assessing water and climate related risks and trends therein. Given the critical importance of changes in land water availability and of impacts of changes in snow, soil moisture and land surface states for the projected evolution of climate mean and extremes, we expect that LS3MIP will help the research community make fundamental advances in this area.

7 Data availability

The ofine forcing data for the Land-Hist experiments and output from the model simulations described in this paper

will be distributed through the Earth System Grid Federation (ESGF) with digital object identiers (DOIs) assigned. The model output required for LS3MIP is listed in the Appendix. Model data distributed via ESGF will be freely accessible through data portals after registration. This infrastructure makes it possible to carry out the experiments in a distributed matter, and to allow later participation of additional modeling groups. Links to all forcings data sets will be made available via the CMIP Panel website17. Information about accreditation, data infrastructure, metadata structure, citation and acknowledging is provided by Eyring et al. (2016).

17http://www.wcrp-climate.org/index.php/wgcm-cmip/about-cmip

Web End =http://www.wcrp-climate.org/index.php/wgcm-cmip/ http://www.wcrp-climate.org/index.php/wgcm-cmip/about-cmip

Web End =about-cmip

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2824 B. van den Hurk et al.: LS3MIP (v1.0) contribution to CMIP6

Appendix A: Output data tables requested for LS3MIP

Table A1. Variable request table LEday: daily variables related to the energy cycle. Priority index (p ) in column 1 indicates 1: Mandatory and 2: Desirable. The dimension (dim.) column indicates T : time, Y : latitude, X: longitude, and Z: soil or snow layers. Direction

identies the direction of positive numbers.

p Name standard_name (cf) long_name (netCDF) Unit Direction Dim.

1 rss surface_net_downward_shortwave_ux net shortwave radiation W m2 downward T Y X1 rls surface_net_downward_longwave_ux net longwave radiation W m2 downward T Y X2 rsds surface_downwelling_shortwave_ux_in_air downward shortwave radiation W m2 downward T Y X2 rlds surface_downwelling_longwave_ux_in_air downward longwave radiation W m2 downward T Y X2 rsus surface_upwelling_shortwave_ux_in_air upward shortwave radiation W m2 upward T Y X2 rlus surface_upwelling_longwave_ux_in_air upward longwave radiation W m2 upward T Y X1 hs surface_upward_latent_heat_ux latent heat ux W m2 upward T Y X1 hfss surface_upward_sensible_heat_ux sensible heat ux W m2 upward T Y X1 hfds surface_downward_heat_ux ground heat ux W m2 downward T Y X1 hfdsn surface_downeard_heat_ux_in_snow downward heat ux into snow W m2 downward T Y X2 hfmlt surface_snow_and_ice_melt_heat_ux energy of fusion W m2 solid to liquid T Y X2 hfsbl surface_snow_and_ice_sublimation_heat_ux energy of sublimation W m2 solid to vapor T Y X2 tau surface_downward_stress momentum ux N m2 downward T Y X2 hfrs temperature_ux_due_to_rainfall_expressed_ heat transferred to snowpack by rainfall W m2 downward T Y X as_heat_ux_onto_snow_and_ice1 dtes change_over_time_in_thermal_energy_ change in surface heat storage J m2 increase T Y X content_of_surface1 dtesn change_over_time_in_thermal_energy_ change in snow/ice cold content J m2 increase T Y X content_of_surface_snow_and_ice1 ts surface_temperature average surface temperature K T Y X2 tsns surface_snow_skin_temperature snow surface temperature K T Y X2 tcs surface_canopy_skin_temperature vegetation canopy temperature K T Y X2 tgs surface_ground_skin_temperature temperature of bare soil K T Y X2 tr surface_radiative_temperature surface radiative temperature K T Y X1 albs surface_albedo surface albedo T Y X1 albsn snow_and_ice_albedo snow albedo T Y X1 snc surface_snow_area_fraction snow covered fraction T Y X2 albc canopy_albedo canopy albedo T Y X2 cnc surface_canopy_area_fraction canopy covered fraction T Y X1 tsl soil_temperature average layer soil temperature K T ZY X 1 tsnl snow_temperature temperature prole in the snow K T ZY X 1 tasmax air_temperature_maximum daily maximum near-surface air K T Y X temperature

1 tasmin air_temperature_minimum daily minimum near-surface air K T Y X temperature2 clt cloud_area_fraction total cloud fraction T Y X

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Table A2. Variable request table LWday: daily variables related to the water cycle.

p Name standard_name (cf) long_name (netCDF) Unit Direction Dim.

1 pr precipitation_ux precipitation rate kg m2 s1 downward T Y X 2 prra rainfall_ux rainfall rate kg m2 s1 downward T Y X 2 prsn snowfall_ux snowfall rate kg m2 s1 downward T Y X 2 prrc convective_rainfall_ux convective rainfall rate kg m2 s1 downward T Y X 2 prsnc convective_snowfall_ux convective snowfall rate kg m2 s1 downward T Y X 1 prveg precipitation_ux_onto_canopy precipitation onto canopy kg m2 s1 downward T Y X 1 et surface_evapotranspiration total evapotranspiration kg m2 s1 upward T Y X 1 ec liquid_water_evaporation_ux_from_canopy interception evaporation kg m2 s1 upward T Y X 1 tran Transpiration vegetation transpiration kg m2 s1 upward T Y X 1 es liquid_water_evaporation_ux_from_soil bare soil evaporation kg m2 s1 upward T Y X 2 eow liquid_water_evaporation_ux_from_open_water open water evaporation kg m2 s1 upward T Y X 2 esn liquid_water_evaporation_ux_from_surface_snow snow evaporation kg m2 s1 upward T Y X 2 sbl surface_snow_and_ice_sublimation_ux snow sublimation kg m2 s1 upward T Y X 2 slbnosn sublimation_amount_assuming_no_snow sublimation of the snow free area kg m2 s1 upward T Y X 2 potet water_potential_evapotranspiration_ux potential evapotranspiration kg m2 s1 upward T Y X 1 mrro runoff_ux total runoff kg m2 s1 out T Y X 2 mrros surface_runoff_ux surface runoff kg m2 s1 out T Y X 1 mrrob subsurface_runoff_ux subsurface runoff kg m2 s1 out T Y X 1 snm surface_snow_and_ice_melt_ux snowmelt kg m2 s1 solid to liquid T Y X 1 snrefr surface_snow_and_ice_refreezing_ux refreezing of water in the snow kg m2 s1 liquid to solid T Y X 2 snmsl surface_snow_melt_ux_into_soil_layer water owing out of snowpack kg m2 s1 out T Y X 2 qgwr water_ux_from_soil_layer_to_groundwater groundwater recharge from kg m2 s1 out T Y X soil layer

2 rivo water_ux_from_upstream river inow m3 s1 in T Y X2 rivi water_ux_to_downstream river discharge m3 s1 out T Y X1 dslw change_over_time_in_water_content_of_soil_layer change in soil moisture kg m2 increase T Y X1 dsn change_over_time_in_surface_snow_and_ice_amount change in snow water equivalent kg m2 increase T Y X1 dsw change_over_time_in_surface_water_amount change in surface water storage kg m2 increase T Y X1 dcw change_over_time_in_canopy_water_amount change in interception storage kg m2 increase T Y X2 dgw change_over_time_in_groundwater change in groundwater kg m2 increase T Y X2 drivw change_over_time_in_river_water_amount change in river storage kg m2 increase T Y X1 rzwc water_content_of_root_zone root zone soil moisture kg m2 T Y X1 cw canopy_water_amount total canopy water storage kg m2 T Y X1 snw surface_snow_amount snow water equivalent kg m2 T ZY X 1 snwc canopy_snow_amount SWE intercepted by the vegetation kg m2 T Y X2 lwsnl liquid_water_content_of_snow_layer liquid water in snow pack kg m2 T ZY X 1 sw surface_water_amount_assuming_no_snow surface water storage kg m2 T Y X1 mrlsl moisture_content_of_soil_layer average layer soil moisture kg m2 T ZY X 1 mrsos moisture_content_of_soil_layer moisture in top soil (10cm) layer kg m2 T Y X1 mrsow relative_soil_moisture_content_above_eld_capacity total soil wetness T Y X2 wtd depth_of_soil_moisture_saturation water table depth m T Y X1 tws canopy_and_surface_and_subsurface_water_amount terrestrial water storage kg m2 T Y X2 mrlqso mass_fraction_of_unfrozen_water_in_soil_layer average layer fraction of T ZY X liquid moisture

1 mrfsofr mass_fraction_of_frozen_water_in_soil_layer average layer fraction of T ZY X frozen moisture

2 prrsn mass_fraction_of_rainfall_onto_snow fraction of rainfall on snow. T Y X2 prsnsn mass_fraction_of_snowfall_onto_snow fraction of snowfall on snow. T Y X1 lqsn mass_fraction_of_liquid_water_in_snow snow liquid fraction T ZY X 1 snd surface_snow_thickness depth of snow layer m T Y X1 agesno age_of_surface_snow snow age day T Y X2 sootsn soot_content_of_surface_snow snow soot content kg m2 T Y X2 sic sea_ice_area_fraction ice-covered fraction T Y X2 sit sea_ice_thickness sea-ice thickness m T Y X2 dfr depth_of_frozen_soil frozen soil depth m downward T Y X2 dmlt depth_of_subsurface_melting depth to soil thaw m downward T Y X2 tpf permafrost_layer_thickness permafrost layer thickness m T Y X2 pw liquid_water_content_of_permafrost_layer liquid water content of kg m2 T Y X permafrost layeraerodynamic conductance m s1 - T Y X2 ares aerodynamic_resistance aerodynamic resistance s m1 T Y X

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Table A2. Continued.

p Name standard_name (cf) long_name (netCDF) Unit Direction Dim.

1 nudgincw nudging_increment_of_total_water nudging increment of water kg m2 increase T Y X 1 hur relative_humidity relative humidity % T Y X 1 hurmax relative_humidity_maximum daily maximum near-surface % T Y X relative humidity1 hurmin relative_humidity_minimum daily minimum near-surface % T Y X relative humidity

Table A3. Variable request table LCmon: monthly variables related to the carbon cycle.

p Name standard_name (cf) long_name (netCDF) Unit Direction Dim.

1 gpp gross_primary_productivity_of_carbon gross primary production kg m2 s1 downward T Y X 1 npp net_primary_productivity_of_carbon net primary production kg m2 s1 downward T Y X 1 nep surface_net_downward_mass_ux_of_carbon_ net ecosystem exchange kg m2 s1 downward T Y X dioxide_expressed_as_carbon_due_to_all_land_

processes_excluding_anthropogenic_land_use_change1 ra plant_respiration_carbon_ux autotrophic respiration kg m2 s1 upward T Y X 1 rh heterotrophic_respiration_carbon_ux heterotrophic respiration kg m2 s1 upward T Y X 1 fLuc surface_net_upward_mass_ux_of_carbon_ net carbon mass ux into kg m2 s1 upward T Y X dioxide_expressed_as_carbon_due_to_emission_ atmosphere due to land use from_anthropogenic_land_use_change change1 cSoil soil_carbon_content carbon mass in soil pool kg m2 T Y X 1 cLitter litter_carbon_content carbon mass in litter pool kg m2 T Y X 1 cVeg vegetation_carbon_content carbon mass in vegetation kg m2 T Y X 1 cProduct carbon_content_of_products_of_ carbon mass in products of kg m2 T Y X anthropogenic_land_use_change land use change2 cLeaf leaf_carbon_content carbon mass in leaves kg m2 T Y X 2 cWood wood_carbon_content carbon mass in wood kg m2 T Y X 2 cRoot root_carbon_content carbon mass in roots kg m2 T Y X 2 cMisc miscellaneous_living_matter_carbon_content carbon mass in other living kg m2 T Y X compartments on land2 fVegLitter litter_carbon_ux total carbon mass ux from kg m2 s1 T Y X vegetation to litter

2 fLitterSoil carbon_mass_ux_into_soil_from_litter total carbon mass ux from kg m2 s1 T Y X litter to soil

2 fVegSoil carbon_mass_ux_into_soil_from_ total carbon mass ux from kg m2 s1 T Y X vegetation_excluding_litter vegetation directly to soil

1 treeFrac area_fraction tree cover fraction % T Y X 1 grassFrac area_fraction natural grass fraction % T Y X 1 shrubFrac area_fraction shrub fraction % T Y X 1 cropFrac area_fraction crop fraction % T Y X 1 pastureFrac area_fraction anthropogenic pasture fraction % T Y X 1 baresoilFrac area_fraction bare soil fraction % T Y X 1 residualFrac area_fraction fraction of grid cell that is % T Y X land but neither vegetation-covered nor bare soil1 lai leaf_area_index leaf area index kg m2 T Y X

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Table A4. Variable request table L3hr: 3-hourly variables to generate the atmospheric boundary conditions for the off-line simulation.

p Name standard_name (cf) long_name (netCDF) Unit Direction Dim.

1 rsds surface_downwelling_ downward shortwave radiation W m2 downward T Y X shortwave_ux_in_air

1 rlds surface_downwelling_ downward longwave radiation W m2 downward T Y X longwave_ux_in_air

1 hus specic_humidity near-surface specic humidity kg kg1 T Y X 1 ta air_temperature near-surface air temperature K T Y X 1 ps surface_air_pressure surface pressure Pa T Y X 1 ws wind_speed near-surface wind speed m s1 T Y X 2 va northward_wind near-surface northward wind component m s1 northward T Y X 2 ua eastward_wind near-surface eastward wind component m s1 eastward T Y X 2 pr precipitation_ux precipitation rate kg m2 s1 downward T Y X 1 prra rainfall_ux rainfall rate kg m2 s1 downward T Y X 1 prsn snowfall_ux snowfall rate kg m2 s1 downward T Y X 2 prrc convective_rainfall_ux convective rainfall rate kg m2 s1 downward T Y X 2 prsnc convective_snowfall_ux convective snowfall rate kg m2 s1 downward T Y X 1 clt cloud_area_fraction total cloud fraction T Y X 2 co2c mole_fraction_of_carbon_ near-surface CO2 concentration T Y X dioxide_in_air

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Acknowledgements. The authors thank the CMIP panel of the WCRP Working Group on Climate Modelling for their efforts in coordinating the CMIP6 enterprise. Graham P. Weedon was supported by the Joint UK DECC/Defra Met Ofce Hadley Climate Centre Programme (GA01101). Jiafu Mao is supported by the Biogeochemistry-Climate Feedbacks Scientic Focus Area project funded through the Regional and Global Climate Modeling Program in Climate and Environmental Sciences Division (CESD) of the Biological and Environmental Research (BER) Program in the U.S. Department of Energy (DOE) Ofce of Science. Oak Ridge National Laboratory is managed by UT-BATTELLE for DOE under contract DE-AC05-00OR22725. H. Kim and T. Oki were supported by Japan Society for the Promotion of Science KAKENHI (16H06291). Hanna Lee (NorESM) has expressed intention to participate in LS3MIP when feasible, but has not contributed to this manuscript.

Edited by: J. KalaReviewed by: P. Dirmeyer, G. Abramowitz, and one anonymous referee

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