Surface Air Temperature Biases Research Articles

Arctic soil carbon insulation averts large spring cooling from surface–atmosphere feedbacks

The insulative properties of soil organic carbon (SOC) and surface organic layers (moss, lichens, litter) regulate surface-atmosphere energy exchanges in the Arctic through a coupling with soil temperatures. However, a physical description of this process is lacking in many climate models, potentially biasing their high-latitude climate predictions. Using a coupled surface-atmosphere model, we identified a strong feedback loop between soil insulation, surface air temperature, and snowfall. Without insulation, the latent heat needed for soil ice thawing leads to a late spring and summer cold bias in surface air temperature (above 2 °C) over Arctic regions. The integration of soil insulation eliminates this bias and significantly improves the simulation of permafrost dynamics. Our findings, including the potential consequences of large perturbations (e.g., fires), highlight the importance of combining soil water freezing with a physical representation of SOC and surface organic layer insulation in Earth system models, to improve Arctic climate predictions.

Large-Scale Surface Air Temperature Bias in Summer over the CONUS and Its Relationship to Tropical Central Pacific Convection in the UFS Prototype 8

Abstract This study aims to understand the source of surface air temperature bias over the contiguous United States (CONUS) during boreal summer (June–September) in the Unified Forecast System (UFS) coupled model prototype 8 (P8), developed by the National Centers for Environmental Prediction (NCEP) and the National Oceanic and Atmospheric Administration (NOAA). The focus is on the subseasonal variability defined as a weekly average in weeks 2–5 of forecast leads (total 224 cases; 4 weeks × 2 initialization dates × 4 months × 7 years). The large-scale surface air temperature bias pattern is extracted using the empirical orthogonal function (EOF) analysis. The associated principal component describes the variability of bias for each reforecasting week throughout the 2011–17 reforecasting period. The leading EOF of surface air temperature bias exhibits an east–west dipole pattern over the CONUS, explaining 31.6% of the total variability of weekly temperature bias. This bias pattern is strongly related to the upper-level Rossby wave induced by a bias in convection over the central tropical Pacific. Furthermore, the mean bias of background flow in the extratropics degrades the representation of teleconnections from the tropics to the midlatitudes. UFS P8 has weaker zonal wind over the North Pacific with stronger vertical wind shear than the ERA5 reanalysis. The weak zonal wind hampers the Rossby wave’s propagation, while strong vertical shear reduces its amplitude.

Surface Warming Constraint Projects Less Permafrost Thawing in High Mountain Asia

AbstractReliable projections of permafrost change are crucial for estimating permafrost carbon loss. However, potential model biases in surface air temperature may yield unrealistic projections of future permafrost area. Here, by leveraging the emergent relationship between equilibrium climate sensitivity and projected changes in mean annual air temperature over High Mountain Asia (HMA), we mitigate the overestimated local warming rates and excessive thawing of permafrost associated with the “hot model” problem in models participating in the Coupled Model Intercomparison Project phase 6. After constraint, permafrost area over HMA will reduce by 37%, 64% and 99% in 2081–2100 relative to present‐day under the SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. In contrast, the unconstrained projections tend to overestimate the loss of permafrost area by nearly 10% under the low and mid‐emission scenarios due to the overestimation of local warming rates. These findings are crucial for policymaking and provide valuable insights into global permafrost projection.

Adjustment of the urbanization bias in surface air temperature series based on urban spatial morphologies and using machine learning

Urbanization bias is one of the main systematic biases in surface air temperature (SAT) in climate change monitoring. The traditional linear approach assumes that the impact of urbanization on SAT sequences increases at a uniform rate, but this contradicts the real urbanization processes that occurred around observation stations. This paper puts forward a new adjustment method by using urban spatial morphologies and a random forest (RF) model. Results showed a warming rate of 26.2% (59.1%) during 1991–2020 (2006–2020) that was caused by urbanization. The RF model achieved better performance between urbanization bias and urban morphologies around 4 km buffer areas of the national meteorological stations than other buffers. It was noted that the importance analysis illustrated the urban horizontal morphologies had a stronger explanation for urbanization bias than vertical morphologies. Finally, the importance values were introduced into the traditional adjustment method. Taking Hefei as an example, the adjustment results obtained using the new method reveal that the urbanization bias maintains dynamic consistency with the observed changes in the surrounding environment of the target station. The new adjustment method might help improve our understanding of the relationship between human activities and regional climate change and also would provide technical references for the protection of the observation environment and the construction of reference climate data.

Assessment of climate biases in OpenIFS version 43r3 across model horizontal resolutions and time steps

Abstract. We examine the impact of horizontal resolution and model time step on the climate of the OpenIFS version 43r3 atmospheric general circulation model. A series of simulations for the period 1979–2019 are conducted with various horizontal resolutions (i.e. ∼100, ∼50, and ∼25 km) while maintaining the same time step (i.e. 15 min) and using different time steps (i.e. 60, 30, and 15 min) at 100 km horizontal resolution. We find that the surface zonal wind bias is significantly reduced over certain regions such as the Southern Ocean and the Northern Hemisphere mid-latitudes and in tropical and subtropical regions at a high horizontal resolution (i.e. ∼25 km). Similar improvement is evident too when using a coarse-resolution model (∼100 km) with a smaller time step (i.e. 30 and 15 min). We also find improvements in Rossby wave amplitude and phase speed, as well as in weather regime patterns, when a smaller time step or higher horizontal resolution is used. The improvement in the wind bias when using the shorter time step is mostly due to an increase in shallow and mid-level convection that enhances vertical mixing in the lower troposphere. The enhanced mixing allows frictional effects to influence a deeper layer and reduces wind and wind speed throughout the troposphere. However, precipitation biases generally increase with higher horizontal resolutions or smaller time steps, whereas the surface air temperature bias exhibits a small improvement over North America and the eastern Eurasian continent. We argue that the bias improvement in the highest-horizontal-resolution (i.e. ∼25 km) configuration benefits from a combination of both the enhanced horizontal resolution and the shorter time step. In summary, we demonstrate that, by reducing the time step in the coarse-resolution (∼100 km) OpenIFS model, one can alleviate some climate biases at a lower cost than by increasing the horizontal resolution.

Evaluation of Sea Ice Simulation of CAS-ESM 2.0 in Historical Experiment

A sea ice model is an important component of an Earth system model, which is an essential tool for the study of sea ice, including its internal processes, interactions with other components, and projected future changes. This paper evaluates a simulation of sea ice by the Chinese Academy of Sciences Earth System Model version 2 (CAS-ESM 2.0), focusing on a historical simulation in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared with the observations, CAS-ESM 2.0 reproduces reasonable seasonal cycle features and the climatological spatial distribution of Arctic and Antarctic sea ice, including sea ice extent (SIE), sea ice concentration, and sea ice thickness and motion. However, the SIE in CAS-ESM 2.0 is too large in winter and too low in summer in both hemispheres, indicating higher seasonal variations of the model relative to observations. Further sea ice mass budget diagnostics show that basal growth contributes most to ice increase in both hemispheres, basal melt and top melt make a comparable contribution to Arctic ice decrease, and basal melt plays a dominant role in Antarctic ice loss. This, combined with surface air temperature (SAT) and sea surface temperature (SST) biases, suggests that the excess of sea ice simulated in wintertime in both hemispheres and the lower SIE simulated in the Antarctic summer are mainly attributable to the bias in SST, whereas the lower SIE simulated in the Arctic summer is probably due to the combined effects of both the SST and SAT biases.

Influence of Changes in Meteorological Observational Environment on Urbanization Bias in Surface Air Temperature: A Review

It is important to quantify changes in the local meteorological observational environment (MOE) around weather stations if we are to obtain accurate assessments of the regional warming of the surface air temperature (SAT) in relation to urbanization bias. Current studies often use two-dimensional parameters (e.g., the land surface temperature, land use/land cover and the normalized difference vegetation index) to characterize the local MOE. Most of the existing models of the relationship between urbanization bias in SAT series and MOE parameters are linear regression models, which ignore the non-linear driving effect of MOE changes on SAT series. By contrast, there is a lack of three-dimensional parameters in the characterization of the morphological features of the MOE. Changes in the MOE related to urbanization lead to uncertainties in the contribution of SAT series on different scales and we need to introduce vertical structure indexes to enrich the three-dimensional spatial morphology of MOE parameters. The non-linear response of urbanization bias in SAT series to three-dimensional changes in the MOE and its scale dependence should be explored by coupling computational fluid dynamics model simulations with machine learning.

Inclusion of land use changes in long‐term regional climate simulations over East Asia

AbstractLong‐term simulations with regional climate models (RCMs) show biases over East Asia that result partly from fix‐in‐time land use data (EX2001). Using four satellite‐based land use images (EX8901) from the past 30 years instead of default values, 34‐year integrations were performed to make comparisons (EX8901 minus EX2001) over East Asian land areas. The decreased albedo (−0.019) resulted in weakened upward short‐wave flux, contributing to positive surface radiative forcing (2.89 W m−2), which then induced enhanced sensible heat fluxes. The near‐surface wind speed was weakened (−0.15 m s−1) in relation to the increased roughness length (0.042 m) and weakened pressure gradient, and induced a decreased precipitation bias of 0.0095 mm day−1. The increased ground temperature and enhanced soil water content promoted strengthened sensible and latent heat fluxes, which induced a decreased surface air temperature bias (−0.24 °C). Results show that by including land use changes, model simulations can be improved.

Impact of Model Physics on Seasonal Forecasts of Surface Air Temperature in the Arctic

The impacts of model physics and initial sea ice thickness on seasonal forecasts of surface energy budget and air temperature in the Arctic during summer were investigated based on Climate Forecast System, version 2 (CFSv2), simulations. The model physics changes include the enabling of a marine stratus cloud scheme and the removal of the artificial upper limit on the bottom heat flux from ocean to sea ice. The impact of initial sea ice thickness was examined by initializing the model with relatively realistic sea ice thickness generated by the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Model outputs were compared to that from a control run that did not impose physics changes and used Climate Forecast System Reanalysis (CFSR) sea ice thickness. After applying the physics modification to either sea ice thickness initialization, the simulated total cloud cover more closely resembled the observed monthly variations of total cloud cover except for the midsummer reduction. Over the Chukchi–Bering Seas, the model physics modification reduced the seasonal forecast bias in surface air temperature by 24%. However, the use of initial PIOMAS sea ice thickness alone worsened the surface air temperature predictions. The experiment with physics modifications and initial PIOMAS sea ice thickness achieves the best surface air temperature improvement over the Chukchi–Bering Seas where the area-weighted forecast bias was reduced by 71% from 1.05 K down to −0.3 K compared with the control run. This study supports other results that surface temperatures and sea ice characteristics are highly sensitive to the Arctic cloud and radiation formulations in models and need priority in model formulation and validation.

Land Surface Climate in the Regional Arctic System Model

AbstractThe Regional Arctic System Model (RASM) is a fully coupled, regional Earth system model applied over the pan-Arctic domain. This paper discusses the implementation of the Variable Infiltration Capacity land surface model (VIC) in RASM and evaluates the ability of RASM, version 1.0, to capture key features of the land surface climate and hydrologic cycle for the period 1979–2014 in comparison with uncoupled VIC simulations, reanalysis datasets, satellite measurements, and in situ observations. RASM reproduces the dominant features of the land surface climatology in the Arctic, such as the amount and regional distribution of precipitation, the partitioning of precipitation between runoff and evapotranspiration, the effects of snow on the water and energy balance, and the differences in turbulent fluxes between the tundra and taiga biomes. Surface air temperature biases in RASM, compared to reanalysis datasets ERA-Interim and MERRA, are generally less than 2°C; however, in the cold seasons there are local biases that exceed 6°C. Compared to satellite observations, RASM captures the annual cycle of snow-covered area well, although melt progresses about two weeks faster than observations in the late spring at high latitudes. With respect to derived fluxes, such as latent heat or runoff, RASM is shown to have similar performance statistics as ERA-Interim while differing substantially from MERRA, which consistently overestimates the evaporative flux across the Arctic region.

Biases of the Arctic climate in a regional ocean-sea iceatmosphere coupled model: an annual validation

The Coupling of three model components, WRF/PCE (polar climate extension version of weather research and forecasting model (WRF)), ROMS (regional ocean modeling system), and CICE (community ice code), has been implemented, and the regional atmosphere-ocean-sea ice coupled model named WRF/PCE-ROMS-CICE has been validated against ERA-interim reanalysis data sets for 1989. To better understand the reasons that generate model biases, the WRF/PCE-ROMS-CICE results were compared with those of its components, the WRF/PCE and the ROMS-CICE. There are cold biases in surface air temperature (SAT) over the Arctic Ocean, which contribute to the sea ice concentration (SIC) and sea surface temperature (SST) biases in the results of the WRF/PCE-ROMS-CICE. The cold SAT biases also appear in results of the atmospheric component with a mild temperature in winter and similar temperature in summer. Compared to results from the WRF/PCE, due to influences of different distributions of the SIC and the SST and inclusion of interactions of air-sea-sea ice in the WRF/PCE-ROMS-CICE, the simulated SAT has new features. These influences also lead to apparent differences at higher levels of the atmosphere, which can be thought as responses to biases in the SST and sea ice extent. There are similar atmospheric responses in feature of distribution to sea ice biases at 700 and 500 hPa, and the strength of responses weakens when the pressure decreases in January. The atmospheric responses in July reach up to 200 hPa. There are surplus sea ice extents in the Greenland Sea, the Barents Sea, the Davis Strait and the Chukchi Sea in winter and in the Beaufort Sea, the Chukchi Sea, the East Siberian Sea and the Laptev Sea in summer in the ROMS-CICE. These differences in the SIC distribution can all be explained by those in the SST distributions. These features in the simulated SST and SIC from ROMS-CICE also appear in the WRF/PCE-ROMS-CICE. It is shown that the performance of the WRF/PCE-ROMS-CICE is determined to a large extent by its components, the WRF/PCE and the ROMS-CICE.

Simulations of summer monsoon climate over East Asia with a Regional Climate Model (RegCM) using Tiedtke convective parameterization scheme (CPS)

In this study, we implemented the Tiedtke convective parameterization scheme (CPS) into the Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climate Model version 3 (RegCM3) and simulated the East Asian Summer Monsoon (EASM) climate. A 6-year experiment was completed, from September 1996 through August 2002, and compared with an analogous experiment employing the Grell CPS option available in RegCM3. The ability of the model to represent the average climatology was investigated. Our results indicate that the Tiedtke CPS shows a generally good performance in describing surface climate and large-scale circulation throughout the summer monsoon period. Compared to the simulation with Grell CPS, the simulation with Tiedtke scheme shows a number of improvements, including a better distribution of summer monsoon precipitation due to a better positioning of the Western Pacific Subtropical High (WPSH) in the middle troposphere and the southwesterly jet in the lower troposphere, and more realistic seasonal evolution of the monsoon precipitation. The cold surface air temperature bias characteristic frequently seen in Grell scheme over this region is also reduced. Generally, the Tiedtke scheme simulates warm and wet atmospheric conditions in the middle and lower tropospheres, a result more in agreement with the European Centre for Medium-Range Weather Forecasts (ECMWF) 40Years analysis (ERA-40). The Tiedtke scheme is more prone to activate convection in the lower troposphere than the Grell scheme due to more moist static energy available for activating and supporting the development of convection systems.

Modeling the Summertime Climate of Southwest Asia: The Role of Land Surface Processes in Shaping the Climate of Semiarid Regions

Abstract Presented is a study on the role of land surface processes in determining the summertime climate over the semiarid region of southwest Asia. In this region, a warm surface air temperature bias of 3.5°C is simulated in the summer by using the standard configuration of Regional Climate Model version 3 (RegCM3). Biases are also simulated in surface albedo (underestimation), shortwave incident radiation (overestimation), and vapor pressure (underestimation). Based on satellite measurements documented in NASA’s surface radiation budget (SRB) dataset, a correction in surface albedo by 4% is introduced in RegCM3 to match the observed SRB data. Increasing albedo values results in a nearly 1°C cooling over the region. In addition, by incorporating RegCM3’s dust module and including subgrid variability for surface wind, shortwave incident radiation bias originally of about 45 W m−2 is reduced by 30 W m−2. As a result, the reduction of shortwave incident radiation cools the surface by 0.6°C. Finally, including a representation for the irrigation and marshlands of Mesopotamia produces surface relative humidity values closer to observations, thus eliminating a nearly 5-mb vapor pressure dry bias over some of the region. Consequently, the representation of irrigation and marshlands results in cooling of nearly 1°C in areas downwind of the actual land-cover change. Along with identified biases in observational datasets, these combined processes explain the 3.5°C warm bias in RegCM3 simulations. Therefore, it is found that accurate representations of surface albedo, dust emissions, and irrigation are important in correctly modeling summertime climates of semiarid regions.

Diagnostics of Climate Model Biases in Summer Temperature and Warm-Season Insolation for the Simulation of Regional Paddy Rice Yield in Japan

Abstract This study quantifies the ranges of climate model biases in surface air temperature for July and August (summer temperature) and daily total insolation for May–October (warm-season insolation) that can give simulated regional paddy rice yields with a bias within ±2.5% of the 20-yr mean observed regional yield. The following four sets of three meteorological elements (daily maximum and minimum temperatures and daily total insolation) from daily climate model outputs were used as meteorological inputs for a large-scale crop model for irrigated paddy rice: 1) raw climate model outputs of all meteorological elements, 2) bias-corrected temperatures and raw climate model outputs of insolation, 3) bias-corrected insolation and raw climate model outputs of temperatures, and 4) bias-corrected climate model outputs of all meteorological elements. These meteorological inputs were sourced from seven coupled general circulation models, one regional climate model, and one reanalysis dataset. Crop model simulations with artificially biased meteorological inputs were also used. By using the approximation formula derived from these crop model simulation results and the Monte Carlo simulation technique, it was found that climate model outputs with biases within ±0.6°C and ±3% for summer temperature and warm-season insolation, respectively, could result in a simulated regional paddy rice yield with a bias within ±2.5% of the 20-yr mean observed regional yield. The simulated regional yield was less biased not only when the biases of two meteorological inputs were small but also when the cold or warm bias of summer temperature and the overestimation of warm-season insolation were balanced through the crop model processes. The methodology presented here will lead to a better and more comprehensive understanding of the nature of error propagation from a climate model to an application model and will facilitate the selection of climate models suitable for specific applications.

The WAMME regional model intercomparison study

Results from five regional climate models (RCMs) participating in the West African Monsoon Modeling and Evaluation (WAMME) initiative are analyzed. The RCMs were driven by boundary conditions from National Center for Environmental Prediction reanalysis II data sets and observed sea-surface temperatures (SST) over four May–October seasons, (2000 and 2003–2005). In addition, the simulations were repeated with two of the RCMs, except that lateral boundary conditions were derived from a continuous global climate model (GCM) simulation forced with observed SST data. RCM and GCM simulations of precipitation, surface air temperature and circulation are compared to each other and to observational evidence. Results demonstrate a range of RCM skill in representing the mean summer climate and the timing of monsoon onset. Four of the five models generate positive precipitation biases and all simulate negative surface air temperature biases over broad areas. RCM spatial patterns of June–September mean precipitation over the Sahel achieve spatial correlations with observational analyses of about 0.90, but within two areas south of 10°N the correlations average only about 0.44. The mean spatial correlation coefficient between RCM and observed surface air temperature over West Africa is 0.88. RCMs show a range of skill in simulating seasonal mean zonal wind and meridional moisture advection and two RCMs overestimate moisture convergence over West Africa. The 0.5° computing grid enables three RCMs to detect local minima related to high topography in seasonal mean meridional moisture advection. Sensitivity to lateral boundary conditions differs between the two RCMs for which this was assessed. The benefits of dynamic downscaling the GCM seasonal climate prediction are analyzed and discussed.

Diagnosis of the summertime warm and dry bias over the U.S. Southern Great Plains in the GFDL climate model using a weather forecasting approach

Weather forecasts started from realistic initial conditions are used to diagnose the large warm and dry bias over the United States Southern Great Plains simulated by the GFDL climate model. The forecasts exhibit biases in surface air temperature and precipitation within 3 days which appear to be similar to the climate bias. With the model simulating realistic evaporation but underestimated precipitation, a deficit in soil moisture results which amplifies the initial temperature bias through feedbacks with the land surface. The underestimate of precipitation may be associated with an inability of the model to simulate the eastward propagation of convection from the front‐range of the Rocky Mountains and is insensitive to an increase of horizontal resolution from 2° to 0.5° latitude.

Ten-year climatology of summer monsoon over South China and its surroundings simulated from a regional climate model

In a previous study by the authors, a regional climate model (hereafter the RCM) developed to study the summer monsoon over South China (SC) and the South China Sea (SCS) has been tested and found to be able to simulate to a large extent the precipitation over this region for the months of May and June. To examine the interannual variability of the summer monsoon here, it is necessary to establish a model climatology to serve as a comparison and to reduce or even remove any systematic model biases. This paper presents the analyses of such a 10-year climatology (1991–2000). The model was initialized on 1 April and integrated up to the end of June for the ten years. The initial atmospheric conditions and lateral boundary data used in this study are from the European Centre for Medium-range Weather Forecasts ‘40-year’ reanalyses. The RCM can reproduce well the main features of the monsoon circulation and vertical structure of the atmosphere. The RCM can simulate the intensification and northwestward displacement of the south Asian upper anticyclones from May to June, as well as the low-level moisture transport from the Bay of Bengal to SC. In the simulation, the average SCS summer monsoon onset occurs in the fourth pentad of May, which is consistent with the results from previous observational research. In addition, the RCM can reproduce the main characteristics of the onset such as the change of the low-level zonal flow from easterly to westerly as well as the rapid increase in daily precipitation. The SC and SCS precipitation anomalies have the correct sign in almost all the years. The shortcomings of the model simulation include an under-prediction of the strength of the subtropical high over the Northwest Pacific and the moisture transport from the Bay of Bengal to the Indochina Peninsula (IC) and SCS. A cold bias in surface air temperature is also observed, with the 10-year mean biases of the simulated surface air temperature over SC, SCS and IC in May and June being about −2.1 °C, −2.4 °C and −1.4 °C respectively. The 10-year mean biases of the simulated daily precipitation rate over SC, SCS and IC are about 2.0, −3.8 and 3.5 mm d−1 respectively. Copyright © 2005 Royal Meteorological Society.

Regional climate model simulation of U.S. soil temperature and moisture during 1982–2002

The fifth‐generation PSU‐NCAR Mesoscale Model (MM5)‐based regional climate model (CMM5) capability in simulating the U.S. soil temperature and soil moisture annual cycle and interannual variability is evaluated by comparing the 1982–2002 continuous integration driven by the NCEP‐DOE AMIP II reanalysis (R‐2) with observations, the R‐2 derivatives and North American Land Data Assimilation System (NLDAS) products. For the annual cycle, the CMM5 produces more realistic regional details and overall smaller biases than the driving R‐2 and NLDAS outputs. The CMM5 also faithfully simulates interannual variations of soil temperature over the central United States and soil moisture in Illinois and Iowa, where observational data are available. The existing CMM5 differences from observations in soil temperature (moisture) cannot be fully explained by model biases in surface air temperature (precipitation). Inconsistencies between measurements taken under short grass versus model representations beneath other land cover types may play an important role. In particular, such measurements overestimate soil temperature in summer and fall while generating a 1‐month phase lead in the soil moisture annual cycle with respect to croplands in the model. The result emphasizes the need for more comprehensive study on model evaluation and bias understanding of soil temperature and soil moisture.

Mean, interannual variability and trends in a regional climate change experiment over Europe. I. Present-day climate (1961–1990)

We present an analysis of a multidecadal simulation of present-day climate (1961–1990) over Europe with the regional climate model RegCM nested within the global atmospheric model HadAMH. Climatic means, interannual variability and trends are examined, with focus on surface air temperature and precipitation. The RegCM driven by HadAMH fields is able to reproduce the basic features of the observed mean surface climate over Europe, its seasonal evolution and the regional detail due to topographic forcing. Surface air temperature biases are mostly less than 1–2 °C and precipitation biases mostly within 10–20%. The RegCM has more intense vertical transport of temperature and water vapor than HadAMH, which results in lower surface air temperatures and greater precipitation than found in the HadAMH simulation. In some cases this is in the direction of greater agreement with observations, while in others it is in the opposite direction. The simulation shows a tendency to overestimate interannual variability of temperature and precipitation compared to observations, particularly during summer and over the Mediterranean regions. It is shown that in DJF, MAM and SON the RegCM interannual variability is primarily determined by the boundary forcing from HadAMH, while in JJA the internal model physics and resolution effects dominate over many subregions of the domain, and the RegCM has higher interannual variability than HadAMH. The precipitation trends simulated by the nested modeling system for the period 1961–1990 capture some features of the observed trends, in particular the cold season drying over the Mediterranean regions. Ensembles of simulations are, however, needed for a more robust assessment of the model’s capability to simulate climatic trends. Overall, this simulation is of good quality compared with previous nested RegCM experiments and will constitute the basis for the generation of climate change scenarios over the European region to be reported in future work.

Regional Climate Simulation of the 1998 Summer Flood over East Asia

In this study, the severe flood case over East Asia during the 1998 summer was simulated using a regional climate model (SNURCM) with 60 km horizontal resolution (EX60), and the model performance in reproducing the extreme climate events was evaluated. An experiment with higher horizontal resolution of 20 km (EX20) was also performed in order to assess the impact of increased resolution on precipitation simulation of the severe flood.The model reproduced the severe precipitation events occurring in central China in June. In EX60, the temporal and spatial variations of the abnormal Meiyu monsoon fronts, which were well observed were also simulated reasonably except in southern China. The area-averaged daily precipitation and surface air temperatures were underestimated, but their temporal evolutions were in good agreement with observation. In the higher resolution experiment (EX20), simulated downward solar radiation, latent heat flux and convective rain were increased in the major severe rain area over the Yangtze River Basin. The increased precipitation in EX20, which was attributed mainly to the increase of convective rain, resulted in the enhanced precipitation intensity, but only slightly affected total precipitation amounts. The improvement in the higher horizontal resolution simulation appeared in precipitation resulting, in particular, from increased convective activity due to increased latent heat flux at the surface. Nevertheless, the model had significant precipitation bias in some areas with disagreement between the simulated precipitation patterns and distribution, and the observations. The model also had surface air temperature bias resulting from cold biases of the land surface model. With horizontal resolution increased to 20 km, the convective and non-convective precipitation was increased for the late afternoon and early evening time, increasing the total precipitation slightly.