Is land surface processes representation a possible weak link in current Regional Climate Models?

Abstract

The representation of land surface processes and fluxes in climate models critically affects the simulation of near-surface climate over land. Here we present an evaluation of COSMO-CLM 2 , a model which couples the COSMO-CLM Regional Climate Model to the Community Land Model (CLM4.0). CLM4.0 provides a more detailed representation of land processes compared to the native land surface scheme in COSMO-CLM. We perform historical reanalysis-driven simulations over Europe with COSMO-CLM 2 following the EURO-CORDEX intercomparison protocol. We then evaluate simulations performed with COSMO-CLM 2 , the standard COSMO-CLM and other EURO-CORDEX RCMs against various observational datasets of temperature, precipitation and surface fluxes. Overall, the results indicate that COSMO-CLM 2 outperforms both the standard COSMO-CLM and the other EURO-CORDEX models in simulating sensible, latent and surface radiative fluxes as well as 2-meter temperature across different seasons and regions. The performance improvement is particularly strong for turbulent fluxes and for daily maximum temperatures and more modest for daily minimum temperature, suggesting that land surface processes affect daytime even more than nighttime conditions. COSMO-CLM 2 also alleviates a long-standing issue of overestimation of interannual summer temperature variability present in most EURO-CORDEX RCMs. Finally, we show that several factors contribute to these improvements, including the representation of evapotranspiration, radiative fluxes and ground heat flux. Overall, these results demonstrate that land processes represent a key area of development to tackle current deficiencies in RCMs.

Heat stress increase under climate change twice as large in cities as in rural areas: A study for a densely populated midlatitude maritime region

“…In agreement to the evaluation of the climate model in earlier studies [Brisson et al, 2016a[Brisson et al, , 2016bWouters et al, 2015Wouters et al, , 2016Trusilova et al, 2016;Demuzere et al, 2017;Davin et al, 2016], the control simulation was found to reproduce both the observed coarse temperature climatology and the urban heat islands of the study domain very well, see Figures S2, S3, S4, and Table S4. A detailed description and evaluation of the urban climate model and its control configuration is provided in the supporting information S1 (see Texts S1 to S4) [WMO, 2008;Davin et al, 2016;Davy and Esau, 2014;Jacob et al, 2007;Wouters et al, 2013;Dimitrova et al, 2016;Thiery et al, 2016;Vanden Broucke et al, 2015;Akkermans et al, 2014;Davin et al, 2014;Grossman-Clarke et al, 2016;Prein et al, 2013;Grasselt, 2008;Schulz et al, 2016;Haylock et al, 2008;De Ridder et al, 2015]. Even though the model has a very good skill in accordance to previous (CPM) model evaluations, the threshold-based heat stress indicator is very sensitive to the model bias.…”

“…The downscaling strategy takes the lateral boundary conditions from the ERA‐Interim‐driven COSMO‐CLM simulation at 12.5 km resolution from the COordinated Regional climate Downscaling EXperiment (CORDEX) for Europe [ Kotlarski et al , ; Jacob et al , ; Vautard et al , ]. In agreement to the evaluation of the climate model in earlier studies [ Brisson et al , , ; Wouters et al , , ; Trusilova et al , ; Demuzere et al , ; Davin et al , ], the control simulation was found to reproduce both the observed coarse temperature climatology and the urban heat islands of the study domain very well, see Figures S2, S3, S4, and Table S4. A detailed description and evaluation of the urban climate model and its control configuration is provided in the supporting information S1 (see Texts S1 to S4) [ WMO , ; Davin et al , ; Davy and Esau , ; Jacob et al , ; Wouters et al , ; Dimitrova et al , ; Thiery et al , ; Vanden Broucke et al , ; Akkermans et al , ; Davin et al , ; Grossman‐Clarke et al , ; Prein et al , ; Grasselt , ; Schulz et al , ; Haylock et al , ; De Ridder et al , ].…”

Soil Moisture‐Temperature Coupling in a Set of Land Surface Models

“…Despite this consistency in the location of global hot spots of soil moisture-temperature coupling found by previous studies, there are considerable differences in the magnitudes and regional patterns of coupling that have been reported (Dirmeyer, 2011;Koster et al, 2006). Understanding model differences in the representation of soil moisture-temperature coupling is crucial, as inaccurate representation of such land-atmosphere interactions may result in errors and biases in climate projections (Davin et al, 2016;Fischer et al, 2012). At the same time, a better representation of surface soil moisture in atmospheric models has shown to improve weather forecasts (e.g., Bisselink et al, 2011;Orth et al, 2016;Quesada et al, 2012;van den Hurk et al, 2012) and constrain predictions of future climate variability (Sippel et al, 2016;van den Hurk et al, 2016;Vogel et al, 2017).…”

“…Understanding model differences in the representation of soil moisture‐temperature coupling is crucial, as inaccurate representation of such land‐atmosphere interactions may result in errors and biases in climate projections (Davin et al, ; Fischer et al, ). At the same time, a better representation of surface soil moisture in atmospheric models has shown to improve weather forecasts (e.g., Bisselink et al, ; Orth et al, ; Quesada et al, ; van den Hurk et al, ) and constrain predictions of future climate variability (Sippel et al, ; van den Hurk et al, ; Vogel et al, ).…”

Observational uncertainty and regional climate model evaluation: A pan‐European perspective

“…In evaluation exercises, these shortcomings of the reference inevitably influence the performance assessment of climate models and introduce uncertainties in the evaluation results. Previous works have addressed this issue by employing multiple reference data sources for global and regional climate model (GCM and RCM, respectively) evaluation (Kotlarski et al, 2005;Bellprat et al, 2012;Di Luca et al, 2012;Gómez-Navarro et al, 2012;Kotlarski et al, 2012;Maraun et al, 2012;Casanueva et al, 2013;Haslinger et al, 2013;Sunyer et al, 2013;Addor and Fischer, 2015;Brienen et al, 2016;Bucchignani et al, 2016;Cheneka et al, 2016;Davin et al, 2016;Ring et al, 2016;Prein and Gobiet, 2017). Besides quantifying the influence of observational uncertainty on individual model performance scores, two of these studies (Gómez-Navarro et al, 2012;Sunyer et al, 2013) also explicitly address the modification of model ranks when changing the observational reference.…”