ESMValTool (v1.0) – a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP

A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations. The priority of the effort so far has been to target specific scientific themes focusing on selected essential climate variables (ECVs), a range of known systematic biases common to ESMs, such as coupled tropical climate variability, monsoons, Southern Ocean processes, continental dry biases, and soil hydrology–climate interactions, as well as atmospheric CO2 budgets, tropospheric and stratospheric ozone, and tropospheric aerosols. The tool is being developed in such a way that additional analyses can easily be added. A set of standard namelists for each scientific topic reproduces specific sets of diagnostics or performance metrics that have demonstrated their importance in ESM evaluation in the peer-reviewed literature. The Earth System Model Evaluation Tool (ESMValTool) is a community effort open to both users and developers encouraging open exchange of diagnostic source code and evaluation results from the Coupled Model Intercomparison Project (CMIP) ensemble. This will facilitate and improve ESM evaluation beyond the state-of-the-art and aims at supporting such activities within CMIP and at individual modelling centres. Ultimately, we envisage running the ESMValTool alongside the Earth System Grid Federation (ESGF) as part of a more routine evaluation of CMIP model simulations while utilizing observations available in standard formats (obs4MIPs) or provided by the user

The added value of high resolution in estimating the surface mass balance in southern Greenland

The polar version of the regional climate model RACMO2, version 2.3p1, is used to study the effect of model resolution on the simulated climate and surface mass balance (SMB) of south Greenland for the current climate (2007–2014). The model data at resolutions of 60, 20, 6.6, and 2.2 km are intercompared and compared to SMB observations using three different data refinement methods: nearest neighbour, bilinear interpolation, and a statistical downscaling method utilising the local dependency of fields on elevation. Furthermore, it is estimated how the errors induced by model resolution compare to errors induced by the model physics and initialisation. The results affirm earlier studies that SMB components which are tightly linked to elevation, like runoff, can be refined successfully, as soon as the ablation zone is reasonably well resolved in the source dataset. Precipitation fields are also highly elevation dependent, but precipitation has no systematic correlation with elevation, which inhibits statistical downscaling to work well. If refined component-wise, 20 km resolution model simulations can reproduce the SMB ablation observations almost as well as the finer-resolution model simulations. Nonetheless, statistical downscaling and regional climate modelling are complementary; the best results are obtained when high-resolution RACMO2 data are statistically refined. Model estimates in the accumulation zone do not benefit from statistical downscaling; hence, a resolution of about 20 km is sufficient to resolve the majority of the accumulation zone of the Greenland Ice Sheet with respect to the limited measurements we have. Furthermore, we demonstrate that using RACMO2, a hydrostatic model, at 2.2 km resolution led to invalid results as topographic and synoptic vertical winds exceed 10 m s−1, which violates the hydrostatic model assumptions. Finally, additional tests show that model resolution is as important as properly resolving spatial albedo patterns, correctly initialising the firn column, and uncertainties in the modelled precipitation and turbulent exchange

The added value of high resolution in estimating the surface mass balance in southern Greenland

The polar version of the regional climate model RACMO2, version 2.3p1, is used to study the effect of model resolution on the simulated climate and surface mass balance (SMB) of south Greenland for the current climate (2007–2014). The model data at resolutions of 60, 20, 6.6, and 2.2 km are intercompared and compared to SMB observations using three different data refinement methods: nearest neighbour, bilinear interpolation, and a statistical downscaling method utilising the local dependency of fields on elevation. Furthermore, it is estimated how the errors induced by model resolution compare to errors induced by the model physics and initialisation. The results affirm earlier studies that SMB components which are tightly linked to elevation, like runoff, can be refined successfully, as soon as the ablation zone is reasonably well resolved in the source dataset. Precipitation fields are also highly elevation dependent, but precipitation has no systematic correlation with elevation, which inhibits statistical downscaling to work well. If refined component-wise, 20 km resolution model simulations can reproduce the SMB ablation observations almost as well as the finer-resolution model simulations. Nonetheless, statistical downscaling and regional climate modelling are complementary; the best results are obtained when high-resolution RACMO2 data are statistically refined. Model estimates in the accumulation zone do not benefit from statistical downscaling; hence, a resolution of about 20 km is sufficient to resolve the majority of the accumulation zone of the Greenland Ice Sheet with respect to the limited measurements we have. Furthermore, we demonstrate that using RACMO2, a hydrostatic model, at 2.2 km resolution led to invalid results as topographic and synoptic vertical winds exceed 10 m s−1, which violates the hydrostatic model assumptions. Finally, additional tests show that model resolution is as important as properly resolving spatial albedo patterns, correctly initialising the firn column, and uncertainties in the modelled precipitation and turbulent exchange

Temperature and Wind Climate of the Antarctic Peninsula as Simulated by a High-Resolution Regional Atmospheric Climate Model

The latest polar version of the Regional Atmospheric Climate Model (RACMO2.3) has been applied to the Antarctic Peninsula (AP). In this study, the authors present results of a climate run at 5.5 km for the period 1979–2013, in which RACMO2.3 is forced by ERA-Interim atmospheric and ocean surface fields, using an updated AP surface topography. The model results are evaluated with near-surface temperature and wind measurements from 12 manned and automatic weather stations and vertical profiles from balloon soundings made at three stations. The seasonal cycle of near-surface temperature and wind is simulated well, with most biases still related to the limited model resolution. High-resolution climate maps of temperature and wind showing that the AP climate exhibits large spatial variability are discussed. Over the steep and high mountains of the northern AP, large west-to-east climate gradients exist, while over the gentle southern AP mountains the near-surface climate is dominated by katabatic winds. Over the flat ice shelves, where katabatic wind forcing is weak, interannual variability in temperature is largest. Finally, decadal trends of temperature and wind are presented, and it is shown that recently there has been distinct warming over the northwestern AP and cooling over the rest of the AP, related to changes in sea ice cover

Contribution of dynamic vegetation phenology to decadal climate predictability

In this study, the impact of coupling and initializing the leaf area index from the dynamic vegetation model Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS) is analyzed on skill of decadal predictions in the fully coupled atmosphere–land–ocean–sea ice model, the European Consortium Earth System Model (EC-Earth). Similar to the impact of initializing the model with the observed oceanic state, initializing the leaf area index (LAI) fields obtained from an offline LPJ-GUESS simulation forced by the observed atmospheric state leads to a systematic drift.A different treatment of the water and soil moisture budget in LPJ-GUESS is a likely cause of this drift. The coupled system reduces the cold bias of the reference model over land by reducing LAI (and the associated evaporative cooling), particularly outside the growing season. The coupling with the interactive vegetation module implies more degrees of freedom in the coupled model, which generates more noise that can mask a portion of the extra signal that is generated. The forecast reliability improves marginally, particularly early in the forecast. Ranked probability skill scores are also improved slightly in most areas analyzed, but the signal is not fully coherent over the forecast interval because of the relatively low number of ensemble members. Methods to remove the LAI drift and allow coupling of other variables probably need to be implemented before significant forecast skill can be expected

Modelling the climate and surface mass balance of polar ice sheets using RACMO2 - Part 1 : Greenland (1958-2016)

We evaluate modelled Greenland ice sheet (GrIS) near-surface climate, surface energy balance (SEB) and surface mass balance (SMB) from the updated regional climate model RACMO2 (1958-2016). The new model version, referred to as RACMO2.3p2, incorporates updated glacier outlines, topography and ice albedo fields. Parameters in the cloud scheme governing the conversion of cloud condensate into precipitation have been tuned to correct inland snowfall underestimation: snow properties are modified to reduce drifting snow and melt production in the ice sheet percolation zone. The ice albedo prescribed in the updated model is lower at the ice sheet margins, increasing ice melt locally. RACMO2.3p2 shows good agreement compared to in situ meteorological data and point SEB/SMB measurements, and better resolves the spatial patterns and temporal variability of SMB compared with the previous model version, notably in the north-east, south-east and along the K-transect in south-western Greenland. This new model version provides updated, high-resolution gridded fields of the GrIS present-day climate and SMB, and will be used for projections of the GrIS climate and SMB in response to a future climate scenario in a forthcoming study

Modelling the climate and surface mass balance of polar ice sheets using RACMO2 - Part 1: Greenland (1958-2016)

We evaluate modelled Greenland ice sheet (GrIS) near-surface climate, surface energy balance (SEB) and surface mass balance (SMB) from the updated regional climate model RACMO2 (1958-2016). The new model version, referred to as RACMO2.3p2, incorporates updated glacier outlines, topography and ice albedo fields. Parameters in the cloud scheme governing the conversion of cloud condensate into precipitation have been tuned to correct inland snowfall underestimation: snow properties are modified to reduce drifting snow and melt production in the ice sheet percolation zone. The ice albedo prescribed in the updated model is lower at the ice sheet margins, increasing ice melt locally. RACMO2.3p2 shows good agreement compared to in situ meteorological data and point SEB/SMB measurements, and better resolves the spatial patterns and temporal variability of SMB compared with the previous model version, notably in the north-east, south-east and along the K-transect in south-western Greenland. This new model version provides updated, high-resolution gridded fields of the GrIS present-day climate and SMB, and will be used for projections of the GrIS climate and SMB in response to a future climate scenario in a forthcoming study.Mathematical Geodesy and Positionin