RegCM4对中国东部区域气候模拟的辐射收支分析

利用卫星和再分析数据,评估了区域气候模式Reg CM4对中国东部地区辐射收支的基本模拟能力,重点关注地表净短波(SNS)、地表净长波(SNL)、大气顶净短波(TNS)、大气顶净长波(TNL)4个辐射分量。结果表明:1)短波辐射的误差值在夏季较大,而长波辐射的误差值在冬季较大。但各辐射分量模拟误差的空间分布在冬、夏季都有较好的一致性。2)对于地表辐射通量,SNS表现为正偏差(向下净短波偏多),在各分量中误差最大,区域平均误差值近50 W/m2;SNL表现为负偏差(向上净长波偏多);对于大气顶辐射通量,TNS和TNL分别表现为"北负南正"的误差分布和整体正偏差。3)利用空间相关和散点线性回归方法对4个辐射分量的模拟误差进行归因分析,发现在云量、地表反照率、地表温度三个直接影响因子中,云量模拟误差的贡献最大,中国东部地区云量模拟显著偏少。     

Improving CLM4.5 simulations of land–atmosphere exchange during freeze–thaw processes on the Tibetan Plateau

Soil is heterogeneous and has different thermal and hydraulic properties, causing varied behavior in heat and moisture transport. Therefore, soil has an important effect on land–atmosphere interactions. In this study, an improved soil parameterization scheme that considers gravel and organic matter in the soil was introduced into CLM4.5 (Community Land Model). By using data from the Zoige and Madoi sites on the Tibetan Plateau, the ability of the model to simultaneously simulate the duration of freeze–thaw periods, soil temperature, soil moisture, and surface energy during freeze–thaw processes, was validated. The results indicated that: (1) the new parameterization performed better in simulating the duration of the frozen, thawing, unfrozen, and freezing periods; (2) with the new scheme, the soil thermal conductivity values were decreased; (3) the new parameterization improved soil temperature simulation and effectively decreased cold biases; (4) the new parameterization scheme effectively decreased the dry biases of soil liquid water content during the freezing, completely frozen, and thawing periods, but increased the wet biases during the completely thawed period; and (5) the net radiation, latent heat flux, and soil surface heat flux of the Zoige and Madoi sites were much improved by the new organic matter and thermal conductivity parameterization.     

Evaluation of high-resolution simulations of daily-scale temperature and precipitation over the United States

Extreme climate events have been increasing over much of the world, and dynamical models predict further increases in response to enhanced greenhouse forcing. We examine the ability of a high-resolution nested climate model, RegCM3, to capture the statistics of daily-scale temperature and precipitation events over the conterminous United States, using observational and reanalysis data for comparison. Our analyses reveal that RegCM3 captures the pattern of mean, interannual variability, and trend in the tails of the daily temperature and precipitation distributions. However, consistent biases do exist, including wet biases in the topographically-complex regions of the western United States and hot biases in the southern and central United States. The biases in heavy precipitation in the western United States are associated with excessively strong surface and low-level winds. The biases in daily-scale temperature and precipitation in the southcentral United States are at least partially driven by biases in circulation and moisture fields. Further, the areas of agreement and disagreement with the observational data are not intuitive from analyzing the simulated mean seasonal temperature and precipitation fields alone. Our evaluation should enable more informed application and improvement of high-resolution climate models for the study of future changes in socially- and economically-relevant temperature and precipitation events.     

Quantifying sources of uncertainty in projected wheat yield changes under climate change in eastern Australia

Future climate projections and impact analyses are pivotal to evaluate the potential change in crop yield under climate change. Impact assessment of climate change is also essential to prepare and implement adaptation measures for farmers and policymakers. However, there are uncertainties associated with climate change impact assessment when combining crop models and climate models under different emission scenarios. This study quantifies the various sources of uncertainty associated with future climate change effects on wheat productivity at six representative sites covering dry and wet environments in Australia based on 12 soil types and 12 nitrogen application rates using one crop model driven by 28 global climate models (GCMs) under two representative concentration pathways (RCPs) at near future period 2021–2060 and far future period 2061–2100. We used the analysis of variance (ANOVA) to quantify the sources of uncertainty in wheat yield change. Our results indicated that GCM uncertainty largely dominated over RCPs, nitrogen rates, and soils for the projections of wheat yield at drier locations. However, at wetter sites, the largest share of uncertainty was nitrogen, followed by GCMs, soils, and RCPs. In addition, the soil types at two northern sites in the study area had greater effects on yield change uncertainty probably due to the interaction effect of seasonal rainfall and soil water storage capacity. We concluded that the relative contributions of different uncertainty sources are dependent on climatic location. Understanding the share of uncertainty in climate impact assessment is important for model choice and will provide a basis for producing more reliable impact assessment.     

Compensating Errors in Cloud Radiative and Physical Properties over the Southern Ocean in the CMIP6 Climate Models

The Southern Ocean is covered by a large amount of clouds with high cloud albedo. However, as reported by previous climate model intercomparison projects, underestimated cloudiness and overestimated absorption of solar radiation (ASR) over the Southern Ocean lead to substantial biases in climate sensitivity. The present study revisits this long-standing issue and explores the uncertainty sources in the latest CMIP6 models. We employ 10-year satellite observations to evaluate cloud radiative effect (CRE) and cloud physical properties in five CMIP6 models that provide comprehensive output of cloud, radiation, and aerosol. The simulated longwave, shortwave, and net CRE at the top of atmosphere in CMIP6 are comparable with the CERES satellite observations. Total cloud fraction (CF) is also reasonably simulated in CMIP6, but the comparison of liquid cloud fraction (LCF) reveals marked biases in spatial pattern and seasonal variations. The discrepancies between the CMIP6 models and the MODIS satellite observations become even larger in other cloud macro- and micro-physical properties, including liquid water path (LWP), cloud optical depth (COD), and cloud effective radius, as well as aerosol optical depth (AOD). However, the large underestimation of both LWP and cloud effective radius (regional means ~20% and 11%, respectively) results in relatively smaller bias in COD, and the impacts of the biases in COD and LCF also cancel out with each other, leaving CRE and ASR reasonably predicted in CMIP6. An error estimation framework is employed, and the different signs of the sensitivity errors and biases from CF and LWP corroborate the notions that there are compensating errors in the modeled shortwave CRE. Further correlation analyses of the geospatial patterns reveal that CF is the most relevant factor in determining CRE in observations, while the modeled CRE is too sensitive to LWP and COD. The relationships between cloud effective radius, LWP, and COD are also analyzed to explore the possible uncertainty sources in different models. Our study calls for more rigorous calibration of detailed cloud physical properties for future climate model development and climate projection.     

Evaluation of Simulated CO_2 Concentrations from the CarbonTracker-Asia Model Using In-situ Observations over East Asia for 2009–2013

The CarbonTracker(CT) model has been used in previous studies for understanding and predicting the sources, sinks, and dynamics that govern the distribution of atmospheric CO_2 at varying ranges of spatial and temporal scales. However, there are still challenges for reproducing accurate model-simulated CO_2 concentrations close to the surface, typically associated with high spatial heterogeneity and land cover. In the present study, we evaluated the performance of nested-grid CT model simulations of CO_2 based on the CT2016 version through comparison with in-situ observations over East Asia covering the period 2009–13. We selected sites located in coastal, remote, inland, and mountain areas. The results are presented at diurnal and seasonal time periods. At target stations, model agreement with in-situ observations was varied in capturing the diurnal cycle. Overall, biases were less than 6.3 ppm on an all-hourly mean basis, and this was further reduced to a maximum of 4.6 ppm when considering only the daytime. For instance, at Anmyeondo, a small bias was obtained in winter, on the order of 0.2 ppm. The model revealed a diurnal amplitude of CO_2 that was nearly flat in winter at Gosan and Anmyeondo stations, while slightly overestimated in the summertime. The model's performance in reproducing the diurnal cycle remains a challenge and requires improvement. The model showed better agreement with the observations in capturing the seasonal variations of CO_2 during daytime at most sites, with a correlation coefficient ranging from 0.70 to 0.99. Also, model biases were within-0.3 and 1.3 ppm, except for inland stations(7.7 ppm).     

Effects of ozone air pollution on crop pollinators and pollination

Human driven environmental changes increase the concentrations of polluting reactive compounds in the troposphere, such as ozone and nitrogen oxides. These changes lead to biodiversity losses and alter plant physiology and plant-pollinator interactions, essential for pollination services, with potential consequences for agricultural production. Here we used 133 unique sampling events from NW Europe to investigate how air pollution (ozone and nitrogen oxides) and other sources of nitrogen is related to pollinator visitation rate and their contribution to agricultural production, also considering possible interactive effects with landscape quality and pesticide input. We showed that ozone modulates the effect of pesticide exposure and temperature on crop pollinators, increasing the probability of negative impacts on crop pollination. Indeed, when ozone levels are highest, the strength of the effect of pesticide on pollinators is more than double then when ozone levels are intermediate. This indicates that air pollution should be considered in management plans and policies aiming to safeguard biodiversity and promote more sustainable food production practices.     

Uncertainties in air quality model predictions

As a result of several air quality model evaluation exercises involving a large number of source scenarios and types of models, it is becoming clear that the magnitudes of the uncertainties in model predictions are similar from one application to another. When considering continuous point sources and receptors at distances of about 0.1 km to 1 km downwind, the uncertainties in ground-level concentration predictions lead to typical mean biases of about ±20 to 40% and typical relative root-mean-square errors of about 60 to 80%. In fact, in two otherwise identical model applications at two independent sites, it is not unusual for the same model to overpredict by 50% at one site and underpredict by 50% at the second site. It is concluded that this fundamental level of model uncertainty is likely to exist due to data input errors and stochastic fluctuations, no matter how sophisticated a model becomes. The tracer studies that lead to these conclusions and have been considered in this study include: (1) tests of the Offshore and Coastal Dispersion (OCD) model at four coastal sites; (2) tests of the Hybrid Plume Dispersion Model (HPDM) at five power plants; (3) tests of a similarity model for near-surface point source releases at four sites; and (4) tests of 14 hazardous gas models at eight sites including six sets of experiments where dense gases were released.     

Circulation statistics and climate change in Central Europe: PRUDENCE simulations and observations

PRUDENCE simulations of the climate in Central Europe are analysed with respect to mean temperature, mean precipitation and three monthly mean geostrophic circulation indices. The three global models show important circulation biases in the control climate, in particular in the strength of the west-circulations in winter and summer. The nine regional models inherit much of the circulation biases from their host model, especially in winter. In summer, the regional models show a larger spread in circulation statistics, depending on nesting procedures and other model characteristics. Simulated circulation biases appear to have a significant inluence on simulated temperature and precipitation. The PRUDENCE ensemble appears to be biased towards warmer and wetter than observed circulations in winter, and towards warmer and dryer circulations in summer. A2-scenario simulations show important circulation changes, which have a significant impact on changes in the distributions of monthly mean temperature and precipitation. It is likely that interactions between land–surface processes and atmospheric circulation play an important role in the simulated changes in the summer climate in Central Europe.     

Biases in AMIP model simulations of the east China monsoon system

 AMIP model simulations of the east China (5–50°N; 105–122°E) monsoon system are analyzed to study coherent relationships between rainfall and wind annual cycle biases. A comparison with observed interannual variability patterns is carried out to identify the physical processes that explain the biases. The analyses show that poleward displacement of the simulated east Asian jet stream causes the ascending branch of the jet-induced transverse circulation to move north and, as a consequence, produces negative (positive) rainfall biases occur in central (northeast) China. The model simulations show decreased southwesterly flow and ITCZ rainfall over the South China Sea when weaker (versus observations) summer Hadley and Walker circulations are present. This results from diminished model tropical disturbance activity, and highlights the importance of air-sea interactions. In addition, during October–January, intensified model low-level easterlies enhance moisture transport and produce positive local rainfall biases over central and northeast China. Biases in the east China monsoon system are concurrently reflected in the planetary circulation. Enhanced northeast China rainfall results from increased surface pressure over the North Pacific and an amplified zonal pressure gradient along the east China coast. This bias pattern is associated with differences in model representations of topography. On the other hand, the South China Sea experiences an extensive elongated meridional rainfall bias dipole structure that straddles the equator. This is accompanied by a baroclinic vertical pattern over the tropics as well as a barotropic wave train that extends from Australia to the Antarctic, where the teleconnection is likely a direct atmospheric response to tropical convective heating. Received: 20 June 2000 / Accepted: 17 September 2000     

The role of northern Arabian Sea surface temperature biases in CMIP5 model simulations and future projections of Indian summer monsoon rainfall

Many climate models have problems simulating Indian summer monsoon rainfall and its variability, resulting in considerable uncertainty in future projections. Problems may relate to many factors, such as local effects of the formulation of physical parametrisation schemes, while common model biases that develop elsewhere within the climate system may also be important. Here we examine the extent and impact of cold sea surface temperature (SST) biases developing in the northern Arabian Sea in the CMIP5 multi-model ensemble, where such SST biases are shown to be common. Such biases have previously been shown to reduce monsoon rainfall in the Met Office Unified Model (MetUM) by weakening moisture fluxes incident upon India. The Arabian Sea SST biases in CMIP5 models consistently develop in winter, via strengthening of the winter monsoon circulation, and persist into spring and summer. A clear relationship exists between Arabian Sea cold SST bias and weak monsoon rainfall in CMIP5 models, similar to effects in the MetUM. Part of this effect may also relate to other factors, such as forcing of the early monsoon by spring-time excessive equatorial precipitation. Atmosphere-only future time-slice experiments show that Arabian Sea cold SST biases have potential to weaken future monsoon rainfall increases by limiting moisture flux acceleration through non-linearity of the Clausius–Clapeyron relationship. Analysis of CMIP5 model future scenario simulations suggests that such effects are small compared to other sources of uncertainty, although models with large Arabian Sea cold SST biases may suppress the range of potential outcomes for changes to future early monsoon rainfall.     

The influence of tropical sea surface temperatures and precipitation on north Pacific atmospheric blocking

Blocking is a major component of the extratropical climate and any changes in it would be a very important aspect of climate change there. Previous studies have shown that mid-latitude variability such as blocking is sensitive to tropical sea surface temperature (SST) anomalies and to variations in tropical precipitation. Climate models exhibit a wide range of skill in representing blocking, with all models having deficiencies in certain respects. In addition, coupled climate models often exhibit significant biases in both tropical precipitation and tropical and extratropical SSTs. This suggests that tropical systematic biases in coupled climate models may influence the representation of blocking and its sensitivity to climate change. We examine the relationship between winter north Pacific blocking and tropical precipitation and tropical SSTs through the use of idealised SST anomaly experiments. We find that interannual variations in convection over the Maritime Continent and eastern equatorial Pacific regions both influence the central and eastern Pacific winter blocking frequency. In addition, systematic underestimation of tropical rainfall over the Maritime Continent region in climate models can lead to underestimation of time-mean winter Pacific blocking. Finally, the sign, magnitude and variability of tropical SST biases in a coupled model, and their associated effects on tropical precipitation, could influence its representation of northern hemisphere blocking, and thus affect its ability to represent this mode of remotely-forced mid-latitude variability. These results have important implications for model development.     

Dependence of Indian monsoon rainfall on moisture fluxes across the Arabian Sea and the impact of coupled model sea surface temperature biases

The Arabian Sea is an important moisture source for Indian monsoon rainfall. The skill of climate models in simulating the monsoon and its variability varies widely, while Arabian Sea cold sea surface temperature (SST) biases are common in coupled models and may therefore influence the monsoon and its sensitivity to climate change. We examine the relationship between monsoon rainfall, moisture fluxes and Arabian Sea SST in observations and climate model simulations. Observational analysis shows strong monsoons depend on moisture fluxes across the Arabian Sea, however detecting consistent signals with contemporaneous summer SST anomalies is complicated in the observed system by air/sea coupling and large-scale induced variability such as the El Ni?o-Southern Oscillation feeding back onto the monsoon through development of the Somali Jet. Comparison of HadGEM3 coupled and atmosphere-only configurations suggests coupled model cold SST biases significantly reduce monsoon rainfall. Idealised atmosphere-only experiments show that the weakened monsoon can be mainly attributed to systematic Arabian Sea cold SST biases during summer and their impact on the monsoon-moisture relationship. The impact of large cold SST biases on atmospheric moisture content over the Arabian Sea, and also the subsequent reduced latent heat release over India, dominates over any enhancement in the land-sea temperature gradient and results in changes to the mean state. We hypothesize that a cold base state will result in underestimation of the impact of larger projected Arabian Sea SST changes in future climate, suggesting that Arabian Sea biases should be a clear target for model development.     

Indian Ocean SST, evaporation, and precipitation during the South Asian summer monsoon in IPCC-AR4 coupled simulations

The veracity of modeled air–sea interactions in the Indian Ocean during the South Asian summer monsoon is examined. Representative simulations of the twentieth century climate, produced by coupled general circulation models as part of the Intergovernmental Panel on Climate Change Fourth Assessment Report, are the analysis targets along with observational data. The analysis shows the presence of large systematic biases in coupled simulations of boreal summer precipitation, evaporation, and sea surface temperature (SST) in the Indian Ocean, often exceeding 50% of the climatological values. Many of the biases are pervasive, being common to most simulations. The representation of air–sea interactions is also compromised. Coupled models tend to emphasize local forcing in the Indian Ocean as reflected by their large precipitation–SST correlations, at odds with the weak links in observations which suggest the importance of non-local controls. The evaporation–SST correlations are also differently represented, indicating atmospheric control on SST in some models and SST control on evaporation in others. The Indian monsoon rainfall–SST links are also misrepresented: the former is essentially uncorrelated with antecedent and contemporaneous Indian Ocean SSTs in nature, but not so in most of the simulations. Overall, coupled models are found deficient in portraying local and non-local air–sea interactions in the Indian Ocean during boreal summer. In our opinion, current models cannot provide durable insights on regional climate feedbacks nor credible projections of regional hydroclimate variability and change, should these involve ocean–atmosphere interactions in the Indian basin.     

The hiatus and accelerated warming decades in CMIP5 simulations

Observed hiatus or accelerated warming phenomena are compared with numerical simulations from the Coupled Model Intercomparison Project Phase 5 （CMIP5） archives,and the associated physical mechanisms are explored based on the CMIP5 models.Decadal trends in total ocean heat content （OHC） are strongly constrained by net top-of-atmosphere （TOA） radiation.During hiatus decades,most CMIP5 models exhibit a significant decrease in the SST and upper OHC and a significant increase of heat penetrating into the subsurface or deep ocean,opposite to the accelerated warming decades.The shallow meridional overturning of the Pacific subtropical cell experiences a significant strengthening （slowdown） for the hiatus （accelerated warming） decades associated with the strengthened （weakened） trade winds over the tropical Pacific.Both surface heating and ocean dynamics contribute to the decadal changes in SST over the Indian Ocean,and the Indonesian Throughflow has a close relationship with the changes of subsurface temperature in the Indian Ocean.The Atlantic Meridional Overturing Circulation （Antarctic Bottom Water） tends to weaken （strengthen） during hiatus decades,opposite to the accelerated warming decades.In short,the results highlight the important roles of air-sea interactions and ocean circulations for modulation of surface and subsurface temperature.     

Consistency Problem with Tracer Advection in the Atmospheric Model GAMIL

The radon transport test, which is a widely used test case for atmospheric transport models, is carried out to evaluate the tracer advection schemes in the Grid-Point Atmospheric Model of IAP-LASG （GAMIL）. Two of the three available schemes in the model are found to be associated with significant biases in the polar regions and in the upper part of the atmosphere, which implies potentially large errors in the simulation of ozone-like tracers. Theoretical analyses show that inconsistency exists between the advection schemes and the discrete continuity equation in the dynamical core of GAMIL and consequently leads to spurious sources and sinks in the tracer transport equation. The impact of this type of inconsistency is demonstrated by idealized tests and identified as the cause of the aforementioned biases. Other potential effects of this inconsistency are also discussed. Results of this study provide some hints for choosing suitable advection schemes in the GAMIL model. At least for the polax-region-concentrated atmospheric components and the closely correlated chemical species, the Flux-Form Semi-Lagrangian advection scheme produces more reasonable simulations of the large-scale transport processes without significantly increasing the computational expense.     

Biases in the diurnal temperature range in Central Europe in an ensemble of regional climate models and their possible causes

The study examines how regional climate models (RCMs) reproduce the diurnal temperature range (DTR) in their control simulations over Central Europe. We evaluate 30-year runs driven by perfect boundary conditions (the ERA40 reanalysis, 1961–1990) and a global climate model (ECHAM5) of an ensemble of RCMs with 25-km resolution from the ENSEMBLES project. The RCMs’ performance is compared against the dataset gridded from a high-density stations network. We find that all RCMs underestimate DTR in all seasons, notwithstanding whether driven by ERA40 or ECHAM5. Underestimation is largest in summer and smallest in winter in most RCMs. The relationship of the models’ errors to indices of atmospheric circulation and cloud cover is discussed to reveal possible causes of the biases. In all seasons and all simulations driven by ERA40 and ECHAM5, underestimation of DTR is larger under anticyclonic circulation and becomes smaller or negligible for cyclonic circulation. In summer and transition seasons, underestimation tends to be largest for the southeast to south flow associated with warm advection, while in winter it does not depend on flow direction. We show that the biases in DTR, which seem common to all examined RCMs, are also related to cloud cover simulation. However, there is no general tendency to overestimate total cloud amount under anticyclonic conditions in the RCMs, which suggests the large negative bias in DTR for anticyclonic circulation cannot be explained by a bias in cloudiness. Errors in simulating heat and moisture fluxes between land surface and atmosphere probably contribute to the biases in DTR as well.     

气候系统模式FGOALS模拟的南亚夏季风：偏差和原因分析

本文通过与观测和再分析资料的对比,评估了LASG/IAP发展的气候系统模式FGOALS的两个版本FGOALS-g2和FGOALS-s2对南亚夏季风的气候态和年际变率的模拟能力,并使用水汽收支方程诊断,研究了造成降水模拟偏差的原因。结果表明,两个模式夏季气候态降水均在陆地季风槽内偏少,印度半岛附近海域偏多,在降水年循环中表现为夏季北侧辐合带北推范围不足。FGOALS-g2中赤道印度洋"东西型"海温偏差导致模拟的东赤道印度洋海上辐合带偏弱,而FGOALS-s2中印度洋"南北型"海温偏差导致模拟的海上辐合带偏向西南。水汽收支分析表明,两个模式中气候态夏季风降水的模拟偏差主要来自于整层积分的水汽通量,尤其是垂直动力平流项的模拟偏差。一方面,夏季阿拉伯海和孟加拉湾的海温偏冷而赤道西印度洋海温偏暖,造成向印度半岛的水汽输送偏少;另一方面,对流层温度偏冷,冷中心位于印度半岛北部对流层上层,同时季风槽内总云量偏少,云长波辐射效应偏弱,对流层经向温度梯度偏弱以及大气湿静力稳定度偏强引起的下沉异常造成陆地季风槽内降水偏少。在年际变率上,观测中南亚夏季风环流和降水指数与Ni?o3.4指数存在负相关关系,但FGOALS两个版本模式均存在较大偏差。两个模式中与ENSO暖事件相关的沃克环流异常下沉支和对应的负降水异常西移至赤道以南的热带中西印度洋,沿赤道非对称的加热异常令两个模式中越赤道环流季风增强,导致印度半岛南部产生正降水异常。ENSO相关的沃克环流异常下沉支及其对应的负降水异常偏西与两个模式对热带南印度洋气候态降水的模拟偏差有关。研究结果表明,若要提高FGOALS两个版本模式对南亚夏季风气候态模拟技巧,需减小耦合模式对印度洋海温、对流层温度及云的模拟偏差;若要提高南亚夏季风和ENSO相关性模拟技巧需要提高模式对热带印度洋气候态降水以及与ENSO相关的环流异常的模拟能力。     

Reducing climate risk for micro-insurance providers in Africa: A case study of Ethiopia

Recurrent climate hazards challenge subsistence farmers in developing countries. Reliance on various diversification strategies and traditional risk sharing among kin and families has serious limitations, such as the problem of covariate risk within such networks. Index-based crop insurance could help to reduce people's climate-related risk, but raising the necessary capital to make insurance schemes financially secure is difficult for micro-insurance providers. We examine the extent to which spatial pooling of micro-insurance schemes could reduce these capital requirements. We simulate a hypothetical insurance market operating in Ethiopia, using rainfall data and yield estimates for 15 stations. By performing a Monte Carlo analysis, risk capital required to keep the probability of financial ruin below a threshold value is identified. We investigate the marginal benefits of pooling increasing numbers of sites, as well as the relationship between the benefits of pooling and the spatial covariance of rainfall. We find spatial diversification to offer considerable savings in required capitalization with as few as three sites pooled, as well as a weak but significant relationship between rainfall covariance and those benefits. The results suggest that spatial pooling may be an attractive option for micro-insurers, worthy of a detailed case-by-case analysis when designing index-insurance schemes.     

Using longwave HIRS radiances to test climate models

A ‘model-to-radiance’ comparison of simulated brightness temperatures from the Hadley Centre Global Environmental Model 2 with measurements from the High Resolution Infrared Radiation Sounder/4 (HIRS/4) instrument onboard the MetOp-A satellite is presented. For the all-sky, the model overestimates brightness temperatures in the atmospheric window region with the greatest biases over areas associated with deep convective cloud. In contrast to many global climate models, much smaller clear-sky biases are found indicating that model clouds are the dominating source of error. Simulated values in upper atmospheric CO₂ channels approximate observations better as a result of compensating cold biases at the poles and warm biases at lower latitudes, due to a poor representation of the Brewer Dobson circulation in the 38 level ‘low-top’ configuration of the model. Simulated all and clear-sky outgoing longwave radiation (OLR) evaluated against the Clouds and the Earth’s Radiant Energy System (CERES) and HIRS OLR products reveal good agreement, in part due to cancellation of positive and negative biases. Through physical arguments relating to the spectral energy balance within a cloud, it is suggested that broadband agreement could be the result of a balance between positive window biases and unseen negative biases originating from the water vapour rotational band in the far infrared (not sampled by HIRS).