中国L波段探空湿度观测资料的质量评估及偏差订正

L波段探空观测资料无论在天气预报还是数值预报中均为最基本和最重要的一类数据,而其湿度观测资料的质量对同化分析及降水预报有直接影响。通过用L波段探空湿度观测资料与不同类型的其他观测反演的湿度资料互校及与NCEP、GRAPES、EC等不同模式分析场为背景的湿度场比较,评估中国L波段探空湿度观测资料的质量状况,对探空湿度资料的质量有了新的认识,为更好地使用该资料提供依据。研究发现中国L波段探空湿度观测资料存在偏干的现象,特别是当背景场湿度大于60%时,观测湿度偏低更加明显。通过分析其偏差特征,找出了适合中国L波段探空湿度观测资料偏差特点的分段函数订正方法。个例试验表明,对探空湿度观测资料的偏差订正后,观测偏差明显减小,订正效果非常显著;模式降水强度预报能力有一定的提高。从连续试验检验的降水预报评分(TS)和预报偏差(Bias)看,中雨和暴雨的预报在探空湿度观测偏差订正后都表现出正效果。     

中国探空观测与第3代再分析大气湿度资料的对比研究

为评估中国探空观测与第3代再分析大气湿度资料的差异,基于ERA-Interim、JRA-55、MERRA和CFSR再分析和中国118探空站1979-2015年逐月850-300 hPa大气比湿和相对湿度的原始值及均一化序列,通过分析探空与再分析资料的相对偏差、相关系数、标准差比和变化趋势,研究了两者在多年平均值、年际变率、离散度及长期变化趋势等方面的差异。结果表明,中国探空原始湿度序列存在显著的非均一性问题,均一化提高了序列的连续性,但存在显著的低偏差,总体较原始湿度偏低5%-43%。再分析中国平均对流层大气比湿和相对湿度较探空观测偏高,相对湿度的偏差幅度（7%-48%）较比湿大（4%-13%）,对流层高层较低层大,春秋季偏差较夏季显著。各再分析资料间的差别较小,JRA-55在对流层高层较其他再分析资料偏低,与探空观测较接近。再分析与均一化后中国探空比湿和相对湿度年际变率和离散度在对流层低层较为一致,对流层中高层再分析资料的离散度明显高于探空。再分析与均一化探空中国平均比湿在对流层低层一致呈上升趋势;对流层中层探空为上升趋势,再分析资料为下降趋势。再分析与均一化探空相对湿度变化趋势差异较大,探空为上升趋势且对流层中高层上升显著,对流层再分析为下降趋势。      

1979-2012年中国探空温度资料中非均一性问题的检验与分析

利用加拿大环境部气候研究中心研发的PMTred(penalized maximum t test)非均一性检验方法,以ERA-interim资料作为参考序列,以中国各探空台站详细的元数据信息为主要断点判断依据,对1979-2012年中国125个探空台站7个标准等压面月平均探空温度资料进行了非均一性检验、订正,并结合详细的元数据信息分析了造成中国探空温度序列非均一问题的主要原因。研究表明,中国探空温度资料中存在由于人为因素造成的断点,整套探空资料中非均一的台站数和断点数所占的百分比呈低层少、高层多的趋势。各标准等压面上月平均温度序列非均一的探空台站平均订正幅度也随着高度的升高而增大,并且订正量为负值在整套订正资料中所占的比例较高,说明中国的探空月平均温度原始观测资料存在系统性偏高的问题。订正后200 hPa的温度变化趋势接近0,以200 hPa为转折点,100 hPa的降温趋势较订正前明显减弱,100 hPa以下为升温趋势,300-700 hPa的增温趋势加强。1979-2012年中国探空月平均温度资料的非均一问题主要来源于2000-2001年探测系统的升级(包括辐射误差订正方法的变化),其次是2002年之后观测仪器的换型。这两次连续的元数据变化均造成了之后中国探空月平均温度出现了系统性的降低,这也是造成订正前后温度变化趋势存在差异的主要原因。     

NCEP再分析资料在强对流环境分析中的应用

为考察NCEP再分析资料在我国强对流天气产生环境分析中的适用性,选取2002—2009年多普勒天气雷达识别的60例超级单体风暴个例,对比分析常规探空资料和NCEP再分析资料提取的温、湿、风垂直廓线,结果表明:NCEP再分析资料计算的对流有效位能因对抬升气团湿度敏感而与观测间差异较大,宜用K指数、温度直减率分析大气层结稳定度;因对流层中高层风与探空差异不大,其中500~700 hPa的风与探空近乎一致,因此NCEP再分析资料计算的深层、中层风垂直切变参量可靠性较高;NCEP再分析资料水汽参数与探空资料差异大,特别是在大气边界层,需用观测资料订正;边界层物理量,特别是风向与探空差异显著,因此不宜用NCEP再分析资料讨论雷暴触发问题;平均而言,NCEP再分析资料湿度廓线低层偏干而中层偏湿,925 hPa以上风速偏小,降低了强对流发生概率。     

下投探空资料在台风莫拉克路径预报的应用试验

2009年8月7日中国大陆举行了首次利用机载下投式探空仪观测台风的试验,飞机在台风莫拉克与天鹅之间的云带相对稀薄区释放11个下投式探空仪。基于下投探空观测资料、常规探空资料和1°×1°分辨率的NCEP再分析资料,分析下投探空资料的可用性,并以下投探空资料初步分析了两台风间南海上空的风场、湿度场等大气特性;分别进行了有无以同化下投探空为初始场的GRAPES模式的模拟试验,以了解下投探空资料对台风莫拉克预报的影响作用。初步结论表明,台风天鹅与莫拉克之间的南海上空对流层中低层为深厚的西南气流,对流层低层及高层湿度小,中间层大;同化下投探空资料后,观测地区(下投探空点及其附近)800 hPa以下西南风减弱,以上加强,湿度中低层减小;有无同化下投探空资料的初值场差异随模式积分向下游传播,影响台风的环境场,改变了台风的引导气流:同化后500 hPa台风引导气流偏东、偏北分量加强,使台风的路径更接近实况路径,48 h台风路径预报误差比原来减少18%。     

春季高原东侧水平稳定层分析

本文利用稠密的探空资料,分析了春季高原东侧的水平稳定层。确定了该稳定层的范围和强度,指出了其温度、湿度和流场特征,并初步探讨了其维持机制。     

往返式探空观测资料的质量控制及不确定性分析

为了推动新型探测资料在数值预报模式中的应用,本文进行了往返式探空资料同化应用前重要的基础性研究工作。基于国内首次往返式探空观测资料,首先建立了面向业务化应用的往返式探空资料质量控制方案,通过对比和分析质量控制前后观测样本的统计特征,论证了质量控制方案的合理性,质量控制后探测要素抽样分布更为合理,要素间一致性得到提高。进而以数值天气预报高时间分辨率的模式预报场和同站址业务常规探空观测资料为参考,分析质量控制后资料的不确定性,结果表明往返探空探测精度达到了世界气象组织WMO（World Meteorological Center）规定的突破目标,部分探测要素甚至实现了理想目标,探测资料具有可用性。最后结合数值模式背景场探讨往返探空资料的可同化性,研究表明往返探空的风场观测和夜间温度观测满足变分同化系统的高斯、无偏假定,可直接同化;气压、湿度和日间温度观测在资料同化前需要开展偏差订正工作,从而更有效的发挥资料价值。本文的研究工作为今后往返探空资料在模式中的同化应用奠定了基础。     

如何提高探空气球的飞升高度

1引言 大气中各高度上气压、温度、湿度随时间和空间分布的资料，对于研究大气中的各种物理过程，以及天气分析和预报等气象服务工作是十分重要的。目前，国内高空探测主要采用由探空气球携带无线电探空仪升空进行压、温、湿的测量。在探空观测中，气球飞升的高度越高，越能取得更大高度范围内的探空资料。所以，如何提高气球的上升高度，获得更完整的气象资料，进一步提高我省的探空业务质量，是每个探空业务人员必须面对和致力研究解决的问题。笔者试图通过对影响气球上升高度因素的分析以及实际工作经验的阐述，供广大探空业务人员参考借鉴，希望能够有所启迪和帮助。     

大气相对湿度资料融合及融合湿度精度检验——基于2013年1月我国中东部地区卫星和探空相对湿度数据

利用2013年1月MODIS(MODerate-resolution Imaging Spectroradiometer)的大气廓线产品计算得到大气相对湿度廓线,并与探空观测得到的相对湿度资料进行对比,发现总体上MODIS反演的相对湿度高于探空值,且在相对湿度较低区域误差较大;需要剔除有云影响的MODIS数据和相对湿度小于5%的探空数据;用平均值订正法对MODIS数据进行均一性修正,采用直接订正法、最大相关系数法开展MODIS和探空相对湿度资料融合试验,对融合结果进行分析对比和精度检验。结果表明:(1)直接订正法融合湿度在相对湿度较低区域高于探空湿度,在相对湿度较高区域低于探空湿度,最大相关系数法融合湿度在高湿区和低湿区均与探空接近;(2)融合结果精度检验表明,直接订正法融合湿度与探空湿度的平均绝对误差在850 hPa不超过10%,在925 hPa不超过12%,在1000 hPa不超过9%;采用最大相关系数法融合得到的相对湿度值在各层与探空湿度的绝对误差均不超过4%。总体来看最大相关系数法比直接订正法融合效果更好。     

同化GPS可降水量资料在安徽地区暴雨预报中的应用

利用2014年5月10日08时和5月24日08时的中国GPS(Global Positioning System)可降水量资料、地面气象观测资料及探空资料,基于WRF(Weather Research Forecast)模式及其三维变分同化系统对安徽地区2014年5月10日区域性暴雨过程和5月24日局地性暴雨过程进行了同化研究。结果表明:同化GPS水汽资料能较好地改善WRF模式的湿度场,对风场的调整较小;同化探空资料和地面常规气象观测资料可以同时调整湿度场与风场,但对湿度场的改善效果明显不如同化GPS水汽资料。仅同化GPS可降水资料,可以改进暴雨落区和强度的WRF模式模拟预报效果;而同化探空资料和地面常规气象观测资料对暴雨落区与强度预报的改进效果低于仅同化GPS水汽资料;同时同化地面常规气象观测资料、探空资料和GPS可降水资料对暴雨强度与落区的预报效果改进明显,与实况更接近,暴雨预报的TS评分更高,空报率更低。     

Oceanic climatology in the coupled model FGOALS-g2: Improvements and biases

The present study examines simulated oceanic climatology in the Flexible Global Ocean-Atmosphere-Land System model, Grid-point Version 2 (FGOALS-g2) forced by historical external forcing data. The oceanic temperatures and circulations in FGOALS-g2 were found to be comparable to those observed, and substantially improved compared to those simulated by the previous version, FGOALS-g1.0. Compared with simulations by FGOALS-g1.0, the shallow mixed layer depths were better captured in the eastern Atlantic and Pacific Ocean in FGOALS-g2. In the high latitudes of the Northern Hemisphere, the cold biases of SST were about 1°C–5°C smaller in FGOALS-g2. The associated sea ice distributions and their seasonal cycles were more realistic in FGOALS-g2. The pattern of Atlantic Meridional Overturning Circulation (AMOC) was better simulated in FGOALS-g2, although its magnitude was larger than that found in observed data. The simulated Antarctic Circumpolar Current (ACC) transport was about 140 Sv through the Drake Passage, which is close to that observed. Moreover, Antarctic Intermediate Water (AAIW) was better captured in FGOALS-g2. However, large SST cold biases (>3°C) were still found to exist around major western boundary currents and in the Barents Sea, which can be explained by excessively strong oceanic cold advection and unresolved processes owing to the coarse resolution. In the Indo-Pacific warm pool, the cold biases were partly related to the excessive loss of heat from the ocean. Along the eastern coast in the Atlantic and Pacific Oceans, the warm biases were due to overestimation of shortwave radiation. In the Indian Ocean and Southern Ocean, the surface fresh biases were mainly due to the biases of precipitation. In the tropical Pacific Ocean, the surface fresh biases (>2 psu) were mainly caused by excessive precipitation and oceanic advection. In the Indo-Pacific Ocean, fresh biases were also found to dominate in the upper 1000 m, except in the northeastern Indian Ocean. There were warm and salty biases (3°C–4°C and 1–2 psu) from the surface to the bottom in the Labrador Sea, which might be due to large amounts of heat transport and excessive evaporation, respectively. For vertical structures, the maximal biases of temperature and salinity were found to be located at depths of >600 m in the Arctic Ocean, and their values exceeded 4°C and 2 psu, respectively.     

Indian Ocean SST Biases in a Flexible Regional Ocean Atmosphere Land System (FROALS) Model

The authors examine the Indian Ocean sea surface temperature(SST) biases simulated by a Flexible Regional Ocean Atmosphere Land System(FROALS) model.The regional coupled model exhibits pronounced cold SST biases in a large portion of the Indian Ocean warm pool.Negative biases in the net surface heat fluxes are evident in the model,leading to the cold biases of the SST.Further analysis indicates that the negative biases in the net surface heat fluxes are mainly contributed by the biases of sensible heat and latent heat flux.Near-surface meteorological variables that could contribute to the SST biases are also examined.It is found that the biases of sensible heat and latent heat flux are caused by the colder and dryer near-surface air in the model.     

Analysis of the surface temperature and wind forecast errors of the NCAR-AirDat operational CONUS 4-km WRF forecasting system

Investigating the characteristics of model-forecast errors using various statistical and object-oriented methods is necessary for providing useful guidance to end-users and model developers as well. To this end, the random and systematic errors (i.e., biases) of the 2-m temperature and 10-m wind predictions of the NCAR-AirDat weather research and forecasting (WRF)-based real-time four-dimensional data assimilation (RTFDDA) and forecasting system are analyzed. This system has been running operationally over a contiguous United States (CONUS) domain at a 4-km grid spacing with four forecast cycles daily from June 2009 to September 2010. In the result an exceptionally useful forecast dataset was generated and used for studying the error properties of the model forecasts, in terms of both a longer time period and a broader coverage of geographic regions than previously studied. Spatiotemporal characteristics of the errors are investigated based on the 24-h forecasts between June 2009 and April 2010, and the 72-h forecasts between May and September 2010. It was found that the biases of both wind and temperature forecasts vary greatly seasonally and diurnally, with dependency on the forecast length, station elevation, geographical location, and meteorological conditions. The temperature showed systematic cold biases during the daytime at all station elevations and warm biases during the nighttime above 1,000 m above sea level (ASL), while below 600 m ASL cold biases occurred during the nighttime. The forecasts of surface wind speed exhibited strong positive biases during the nighttime, while the negative biases were observed in the spring and summer afternoons. The surface wind speed was mostly over-predicted except for the stations located between 1,000 and 2,100 m ASL, for which negative biases were identified for most forecast cycles. The highest wind-speed errors were found over the high terrain and near sea-level stations. The wind-direction errors were relatively large at the high-terrain elevation in the Rocky and Appalachian mountain ranges and the western coastal areas and the error structure exhibited notable diurnal variability.     

Simulation and Improvements of Oceanic Circulation and Sea Ice by the Coupled Climate System Model FGOALS-f3-L

This study documents simulated oceanic circulations and sea ice by the coupled climate system model FGOALS-f3-L developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, under historical forcing from phase 6 of the Coupled Model Intercomparison Project (CMIP6). FGOALS-f3-L reproduces the fundamental features of global oceanic circulations, such as sea surface temperature (SST), sea surface salinity (SSS), mixed layer depth (MLD), vertical temperature and salinity, and meridional overturning circulations. There are notable improvements compared with the previous version, FGOALS-s2, such as a reduction in warm SST biases near the western and eastern boundaries of oceans and salty SSS biases in the tropical western Atlantic and eastern boundaries, and a mitigation of deep MLD biases at high latitudes. However, several obvious biases remain. The most significant biases include cold SST biases in the northwestern Pacific (over 4°C), freshwater SSS biases and deep MLD biases in the subtropics, and temperature and salinity biases in deep ocean at high latitudes. The simulated sea ice shows a reasonable distribution but stronger seasonal cycle than observed. The spatial patterns of sea ice are more realistic in FGOALS-f3-L than its previous version because the latitude–longitude grid is replaced with a tripolar grid in the ocean and sea ice model. The most significant biases are the overestimated sea ice and underestimated SSS in the Labrador Sea and Barents Sea, which are related to the shallower MLD and weaker vertical mixing.     

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。     

Attributing analysis on the model bias in surface temperature in the climate system model FGOALS-s2 through a process-based decomposition method

This study uses the coupled atmosphere–surface climate feedback–response analysis method(CFRAM) to analyze the surface temperature biases in the Flexible Global Ocean–Atmosphere–Land System model, spectral version 2(FGOALS-s2)in January and July. The process-based decomposition of the surface temperature biases, defined as the difference between the model and ERA-Interim during 1979–2005, enables us to attribute the model surface temperature biases to individual radiative processes including ozone, water vapor, cloud, and surface albedo; and non-radiative processes including surface sensible and latent heat fluxes, and dynamic processes at the surface and in the atmosphere. The results show that significant model surface temperature biases are almost globally present, are generally larger over land than over oceans, and are relatively larger in summer than in winter. Relative to the model biases in non-radiative processes, which tend to dominate the surface temperature biases in most parts of the world, biases in radiative processes are much smaller, except in the sub-polar Antarctic region where the cold biases from the much overestimated surface albedo are compensated for by the warm biases from nonradiative processes. The larger biases in non-radiative processes mainly lie in surface heat fluxes and in surface dynamics,which are twice as large in the Southern Hemisphere as in the Northern Hemisphere and always tend to compensate for each other. In particular, the upward/downward heat fluxes are systematically underestimated/overestimated in most parts of the world, and are mainly compensated for by surface dynamic processes including the increased heat storage in deep oceans across the globe.     

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。     

Long-term Stability and Oceanic Mean State Simulated by the Coupled Model FGOALS-s2

We describe the long-term stability and mean climatology of oceanic circulations simulated by version 2 of the Flexible Global Ocean-Atmosphere-Land System model(FGOALS-s2).Driven by pre-industrial forcing,the integration of FGOALS-s2 was found to have remained stable,with no obvious climate drift over 600 model years.The linear trends of sea SST and sea surface salinity(SSS) were 0.04°C(100yr)-1 and 0.01 psu(100yr)-1,respectively.The simulations of oceanic temperatures,wind-driven circulation and thermohaline circulation in FGOALS-s2 were found to be comparable with observations,and have been substantially improved over previous FGOALS-s versions(1.0 and 1.1).However,significant SST biases(exceeding 3°C) were found around strong western boundary currents,in the East China Sea,the Sea of Japan and the Barents Sea.Along the eastern coasts in the Pacific and Atlantic Ocean,a warm bias(>3°C) was mainly due to overestimation of net surface shortwave radiation and weak oceanic upwelling.The difference of SST biases in the North Atlantic and Pacific was partly due to the errors of meridional heat transport.For SSS,biases exceeding 1.5 psu were located in the Arctic Ocean and around the Gulf Stream.In the tropics,freshwater biases dominated and were mainly caused by the excess of precipitation.Regarding the vertical dimension,the maximal biases of temperature and salinity were located north of 65°N at depths of greater than 600 m,and their values exceeded 4°C and 2 psu,respectively.     

Present-day regional climate simulation over Malaysia and western Maritime Continent region using PRECIS forced with ERA40 reanalysis

This study examines the performance of the regional climate model, PRECIS, in reproducing the historical seasonal mean climatology over the Malaysian region. The performance of the model in simulating the seasonal climate pattern of the temperature, precipitation and large-scale circulation was reasonably good. The biases of temperature are less than 2 °C in general, while the seasonal cycles match the observed pattern despite some differences in certain regions. However, the biases for precipitation were greater, particularly over the mountainous areas. These biases could be associated with the deficiencies of the model physics, related to the misrepresentation of the land–surface interaction and convective scheme. Furthermore, the model fails to simulate the mean sea-level pressure over the interior part of Borneo with a significant low-pressure centre. A higher magnitude of the moisture convergence and divergence simulated by the model also contributed to the biases of precipitation over Malaysia.     

A dissection of the surface temperature biases in the Community Earth System Model

Based upon the climate feedback-responses analysis method, a quantitative attribution analysis is conducted for the annual-mean surface temperature biases in the Community Earth System Model version 1 (CESM1). Surface temperature biases are decomposed into partial temperature biases associated with model biases in albedo, water vapor, cloud, sensible/latent heat flux, surface dynamics, and atmospheric dynamics. A globally-averaged cold bias of ?1.22 K in CESM1 is largely attributable to albedo bias that accounts for approximately ?0.80 K. Over land, albedo bias contributes ?1.20 K to the averaged cold bias of ?1.45 K. The cold bias over ocean, on the other hand, results from multiple factors including albedo, cloud, oceanic dynamics, and atmospheric dynamics. Bias in the model representation of oceanic dynamics is the primary cause of cold (warm) biases in the Northern (Southern) Hemisphere oceans while surface latent heat flux over oceans always acts to compensate for the overall temperature biases. Albedo bias resulted from the model’s simulation of snow cover and sea ice is the main contributor to temperature biases over high-latitude lands and the Arctic and Antarctic region. Longwave effect of water vapor is responsible for an overall warm (cold) bias in the subtropics (tropics) due to an overestimate (underestimate) of specific humidity in the region. Cloud forcing of temperature biases exhibits large regional variations and the model bias in the simulated ocean mixed layer depth is a key contributor to the partial sea surface temperature biases associated with oceanic dynamics. On a global scale, biases in the model representation of radiative processes account more for surface temperature biases compared to non-radiative, dynamical processes.