Surface irradiances consistent with CERES-derived top-of-atmosphere shortwave and longwave irradiances

Author Posting. © American Meteorological Society, 2013. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 26 (2013): 2719–2740, doi:10.1175/JCLI-D-12-00436.1.The estimate of surface irradiance on a global scale is possible through radiative transfer calculations using satellite-retrieved surface, cloud, and aerosol properties as input. Computed top-of-atmosphere (TOA) irradiances, however, do not necessarily agree with observation-based values, for example, from the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents a method to determine surface irradiances using observational constraints of TOA irradiance from CERES. A Lagrange multiplier procedure is used to objectively adjust inputs based on their uncertainties such that the computed TOA irradiance is consistent with CERES-derived irradiance to within the uncertainty. These input adjustments are then used to determine surface irradiance adjustments. Observations by the Atmospheric Infrared Sounder (AIRS), Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), CloudSat, and Moderate Resolution Imaging Spectroradiometer (MODIS) that are a part of the NASA A-Train constellation provide the uncertainty estimates. A comparison with surface observations from a number of sites shows that the bias [root-mean-square (RMS) difference] between computed and observed monthly mean irradiances calculated with 10 years of data is 4.7 (13.3) W m−2 for downward shortwave and −2.5 (7.1) W m−2 for downward longwave irradiances over ocean and −1.7 (7.8) W m−2 for downward shortwave and −1.0 (7.6) W m−2 for downward longwave irradiances over land. The bias and RMS error for the downward longwave and shortwave irradiances over ocean are decreased from those without constraint. Similarly, the bias and RMS error for downward longwave over land improves, although the constraint does not improve downward shortwave over land. This study demonstrates how synergetic use of multiple instruments (CERES, MODIS, CALIPSO, CloudSat, AIRS, and geostationary satellites) improves the accuracy of surface irradiance computations.The work was supported by theNASACERES and, in part, Energy Water Cycle Study (NEWS) projects.2013-11-0

Evaluation Of CMIP5 Simulated Clouds And TOA Radiation Budgets Using NASA Satellite Observations

A large degree of uncertainty in global climate models (GCMs) can be attributed to the representation of clouds and how they interact with incoming solar and outgoing longwave (Earth emitted) radiation. In this study, the simulated total cloud fraction (CF), cloud water path (CWP), top-of-atmosphere (TOA) radiation budgets and cloud radiative forcings (CRFs) from 28 CMIP5 AMIP models are evaluated and compared to multiple satellite observations from CERES, MODIS, ISCCP, CloudSat, and CALIPSO. The multimodel ensemble mean CF (58.6 %) is, on global average, under estimated by nearly 7 % compared to CERES-MODIS (CM) and ISCCP results, with an even larger negative bias (16.7 %) compared to the CloudSat/CALIPSO result. The CWP bias is similar in comparison to the CF result; the multimodel ensemble mean is under estimated (16.4 gm−2) when compared to CM. The model simulated and CERES EBAF observed TOA reflected shortwave (SW) and outgoing longwave (LW) radiation fluxes, on average, differ by 1.6 and −0.9 Wm−2, respectively, and is contrary to physical theory. The global averaged SW, LW, and net CRFs form CERES EBAF are −47.2, 26.2, and −21.0 Wm−2, respectively, indicating a net cooling effect due to clouds on the TOA radiation budget. Global biases in the SW and LW CRFs from the multimodel ensemble mean are −1.1 and −1.3 Wm−2, respectively, resulting in a greater net cooling effect of 2.4 Wm−2 in the model simulations. A further investigation of cloud properties and CRFs reveals the GCM biases in atmospheric upwelling (15 °S − 15 °N, ocean-only) regimes are much less than their downwelling (15 ° − 45 °N/S, ocean-only) counterparts. Sensitivity studies have shown that the magnitude of SW cloud radiative cooling increases significantly with increasing CF at similar rates ( −1.20 and −1.31 Wm−2 %−1) in both regimes. The LW cloud radiative warming increases with increasing CF but is regime dependent, demonstrated by the different slopes over the upwelling and downwelling regimes (0.81 and 0.22 Wm %−1, respectively). Through a comprehensive error analysis, we found that CF is a primary modulator of warming (or cooling) in the atmosphere. The comparisons and statistical results from this study may provide helpful insight for improving GCM simulations of clouds and TOA radiation budgets in future versions of CMIP

A Global Investigation Of Cloud-Radiative Properties Through An Integrative Analysis Of Observations And Model Simulations

The cloud and radiative properties simulated in an assortment of global climate models (GCMs) and reanalyses are examined to identify and assess systematic biases based upon comparisons with multiple satellites observations and retrievals. The global mean total column cloud fraction (CF) simulated by the 33-member multimodel mean is 7% and 17% lower than the results from passive (Moderate Resolution Infrared Spectroradiometer, MODIS) and active (CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation, CALIPSO) satellite remote sensing platforms. The simulated cloud water path (CWP), which is used as a proxy for optical depth, on global average, has a negative bias of ~17 g mâ2. Despite these errors in simulated cloud properties, the simulated top-of-atmosphere (TOA) radiation budgets match relatively well with Clouds and the Earth Radiant Energy System (CERES) measurements. The biases in multimodel mean global TOA reflected shortwave (SW) and outgoing longwave (LW) fluxes and cloud radiative effects (CREs) are less than 2.5 W mâ2. Nevertheless, when assessing models individually, some physically inconsistent results are evident. For example, in the ACCESS1.0 model, the simulated TOA SW and LW fluxes are within 2 W mâ2 of the observed global means, however, the global mean CF and CWP are underpredicted by ~10% and ~25 g mâ2, respectively. These unphysical model biases suggest tuning of the modeled radiation budgets. Two dynamically-driven regimes, based on the atmospheric vertical motion at 500 hPa (Ï500), are identified to provide a more quantitative measure of error in the radiation fields determined separately by biases in CF and CWP. These error types include the regime-averaged biases, biases in the sensitivity of TOA CREs to CF/CWP, and their co-variations. Overall, the biases in simulated CF and CWP are larger in the descent regime (Ï500 \u3e 25 hPa dayâ1) than in the ascent regime (Ï500 \u3c â25 hPa dayâ1), but are better correlated with observations. According to CERES observations, the sensitivity of LW CRE to CF is stronger in the ascent regime than in the decent regime (0.82 vs. 0.23 W mâ2 %â1) and the multimodel mean overestimates this value by ~40%. The difference in sensitivity of SW CRE to CF between the two regimes is less drastic (â1.34 vs. â1.12 W mâ2 %â1). TOA CREs rely independently on CWP in regions of large scale ascent and decent, as their sensitivities are similar between these two regimes (e.g., SW CRE/CWP = â0.28 W gâ1 for both regimes). In general, the total TOA CRE errors are heavily weighted by their biases in simulated sensitivity and biases in the simulated CF. A new observationally-constrained, data product is generated that can be used as a process-oriented diagnostic tool to further identify errors in simulated cloud and radiation fields. Based on the CloudSat and CALIPSO Ice Cloud Property Product (2C-ICE), and through one-dimensional radiative transfer modeling, a global database of radiative heating rate profiles is produced for non-precipitating single-layered ice clouds. Non-precipitating single-layered ice clouds have a global occurrence frequency of ~18% with considerable frequency in the tropical upper troposphere (13â16 km). A variety of ice cloud types exist in the sample of single-layered ice clouds developed here, which is determined by the distribution on cloud-top temperatures (CTT). For example, a peak in the distribution near 190 K (260 K) suggests the existence of cirrus (glaciated ice) clouds. The ice cloud microphysical properties responsible for having the largest impact on radiation (e.g., ice water content [IWC] and effective radius [Re]) are largest in the tropics and mid-latitudes according to 2C-ICE. Accordingly, this is where the strongest TOA SW absorption, and subsequently, the strongest upper tropospheric net radiative heating (\u3e 1.5 K dayâ1) occurs. This newly generated product will provide the data for which new ice cloud parameterizations can be developed in global models

관측과 모델 비교를 통한 북극 혼합 구름의 미세물리 특성 및 복사효과 연구

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 지구환경과학부, 2023. 8. 김상우.Clouds have a major impact on the Earth's radiative budget and climate change, yet little microphysical data has been collected on clouds in the polar regions. This lack of microphysics data is related to the challenges of deploying and operating instruments in some of the world's most challenging and remote atmospheric environments. This thesis investigates the macro- and microphysical properties of clouds based on observations over Ny-Ålesund, Svalbard, in order to better understand the role of clouds in the Arctic. The total cloud occurrence was found to be ~77.6% from February 2017 to February 2023. The most predominant cloud type is multilayer clouds with a frequency of occurrence of 39.1%, and single-layer clouds with ~37.2%. The total occurrences of single-layer ice, liquid, and mixed-phase clouds are 19%, 4.4%, and 14.9%, respectively. In addition, surface measurements of upward and downward shortwave and longwave radiation from the Baseline Surface Radiation Network (BSRN) at Ny-Ålesund station were examined. Relatively lower values of upward and downward longwave fluxes for ice and mixed-phase clouds were highly correlated with cloud top temperature by phase. The database of cloud properties and the classification method obtained in this work are used to evaluate weather prediction models. We evaluated the microphysical properties of Arctic low-level clouds simulated by four cloud microphysics parameterization schemes (Morrison, WDM6, NSSL, and P3) implemented in the Polar-optimized Weather Research and Forecasting (PWRF) model. The evaluation is based on a comparison with data from the Arctic Cloud Observations Using Airborne Measurements during the Polar Day (ACLOUD) experiment, which took place near Svalbard in May-June 2017. A significant number of clouds were observed during the campaign, mainly due to adiabatic motions and sensible/latent heat fluxes that caused air masses to warm (by 4°C) as they were transported over the sea ice and ocean transition zone. The Morrison and WDM6 schemes performed best overall, with frequency bias (FB) values close to 1 (1.07, 1.13) and high log-odds ratios (0.50, 0.48) in predicting cloud occurrence, indicating good agreement with observed cloud occurrence. On the other hand, the NSSL and P3 schemes showed a high FB value (1.30, 1.56) with a low log-odds ratio (0.17, 0.16), indicating a high overestimation of cloud occurrence. Conversely, the WDM6 scheme produced higher ice-mixing ratios than the Morrison and NSSL schemes, while the latter two tended to produce more snow and graupel. However, all schemes generally underestimated both liquid and ice water content. Longwave downward (LWD) flux depends on atmospheric temperature and humidity, which are simulated differently by each cloud microphysics scheme. The model underestimated LWD flux is highly correlated with the LWC bias of each scheme. This study highlights the critical need for observational development of cloud parameterization in the Arctic to better estimate the impact of clouds on the Arctic climate under conditions of rapid Arctic warming.북극 증폭에 대한 구름의 영향은 구름의 특성뿐만 아니라 구름이 북극 기후의 구성 요소(수증기, 해양, 해빙, 알베도, 표면 온도 등)와 밀접하게 관련되어 있기 때문에 불확실하다. 이 논문에서는 북극에서 구름의 역할을 더 잘 이해하기 위해 Cloudnet의 스발바드 니알슨 지상 관측자료를 바탕으로 구름의 거시적 및 미시적 특성을 분석하였다. 2017년 2월부터 2023년 2월까지 구름의 총 발생률은 약 77.6%로 나타났다. 가장 우세한 구름 유형은 다층 구름으로 발생 빈도는 39.1%, 단층 구름은 ~37.2%이다. 단층 얼음, 액체, 혼합상 구름의 총 발생 빈도는 각각 19%, 4.4%, 14.9%이다. 또한, 니알슨 관측소의 Baseline Surface Radiation Network (BSRN) 복사 자료를 활용하여 상하 단파 및 장파 복사에 대한 측정값을 분석하였다. 얼음과 혼합상 구름에 대한 장파 상향 및 장파 하향 플럭스의 상대적으로 낮은 값은 위상별 구름 최고 온도와 높은 상관관계를 보였다. 본 연구를 통해 얻은 구름 특성 데이터베이스와 분류 방법은 기상 예측 모델 평가에 사용되었다. 극지에 최적화된 Polar-optimized Weather Research and Forecasting (PWRF) 모델에 구현된 4가지 구름 미세물리 매개변수화 방식(Morrison, WDM6, NSSL, P3)을 활용하여 시뮬레이션한 저층 북극 혼합 구름의 미시물리적 특성을 평가하였다. 이 분석은 2017년 5월부터 6월까지 스발바드 인근에서 진행된 Arctic Cloud Observations Using Airborne Measurements during the Polar Day (ACLOUD) 캠페인 데이터와의 비교를 기반으로 한다. 이 캠페인 기간 동안 상당수의 혼합 구름이 관측되었는데, 이는 주로 해빙과 해양 전이대를 통과하는 동안 기단이 가열(4°C)되는 단열 운동과 현열/잠열 플럭스로 인해 발생했다. Morrison 및 WDM6 구름 모수화 방식은 구름 발생 예측에서 높은 로그 확률(0.50, 0.48)과 함께 1에 가까운 빈도 편향 값(1.07, 1.13)을 나타내며 구름이 발생한 지역 및 높이와 잘 일치하는 것으로 나타나 전반적으로 가장 우수한 성능을 나타났다. NSSL 와 P3 모수화 스킴은 낮은 로그 확률 비율(0.17, 0.16)과 함께 높은 빈도 편향 값(1.30, 1.56)을 보여 구름 발생을 과대평가하는 것으로 나타났다. 반대로 WDM6 모수화 스킴은 Morrison 및 NSSL 방식에 비해 더 높은 얼음 혼합 비율을 생성한 반면, 후자의 두 방식은 더 많은 눈과 싸락눈을 생성하는 경향이 있었다. 모든 방식은 일반적으로 액체와 얼음 수분의 함량을 모두 과소평가했다. 뿐만 아니라, 혼합 구름의 복사율 분석을 통해 구름 타입에 따른 장파와 단파의 상대적 중요도를 평가하고, 구름의 미세물리적 특성에 대한 의존성을 분석하였다. 장파 하강 복사율은 대기 온도와 습도에 따라 달라지며, 모델에서 과소평가된 장파 하강 복사율은 각 구름 모수화 스킴에서 모의된 구름 수분 함량 편향과 높은 상관관계를 보였다. 이 연구는 급격한 북극 온난화 조건에서 구름이 북극 기후에 미치는 영향을 보다 정확하게 추정하기 위해 북극 구름의 타입구분이 중요하며, 북극의 구름 매개 변수화에 대한 관측 기반 개발이 시급히 필요하다.Chapter 1. Introduction １ 1.1 Background and motivation １ 1.2 Scientific Questions ５ 1.3 Objectives of this study ８ Chapter 2. Data and model description ９ 2.1 ACLOUD campaign ９ 2.2 Cloud radar data １１ 2.3 Surface radiation data １３ 2.4 PWRF model configuration １４ Chapter 3. Arctic cloud properties at Ny-Ålesund, Svalbard １９ 3.1 Definition of diagnostics １９ 3.2 Classification of hydrometeors and clouds properties ２４ 3.3 Statistics of Arctic mixed-phase clouds and their radiative effect ３７ Chapter 4. Arctic mixed-phase clouds: comparison between observation and model 4 5 4.1 Meteorological contexts during the ACLOUD campaign ４９ 4.2 Cloud microphysical properties: Observation vs. model ５４ 4.3 Theoretical analysis of scheme algorithm ７７ 4.4 Radiative forcing at the surface ８７ Chapter 5. Summary and future direction ９１ References ９６ 국문 초록 １２１박

Climatology of surface meteorology, surface fluxes, cloud fraction, and radiative forcing over the southeast Pacific from buoy observations

Author Posting. © American Meteorological Society, 2009. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 22 (2009): 5527–5540, doi:10.1175/2009JCLI2961.1.A 5-yr climatology of the meteorology, including boundary layer cloudiness, for the southeast Pacific region is presented using observations from a buoy located at 20°S, 85°W. The sea surface temperature and surface air temperature exhibit a sinusoidal seasonal cycle that is negatively correlated with surface pressure. The relative humidity, wind speed, and wind direction show little seasonal variability. But the advection of cold and dry air from the southeast varies seasonally and is highly correlated with the latent heat flux variations. A simple model was used to estimate the monthly cloud fraction using the observed surface downwelling longwave radiative flux and surface meteorological parameters. The annual cycle of cloud fraction is highly correlated to that of S. A. Klein: lower-tropospheric stability parameter (0.87), latent heat flux (−0.59), and temperature and moisture advection (0.60). The derived cloud fraction compares poorly with the International Satellite Cloud Climatology Project (ISCCP)-derived low-cloud cover but compares well (0.86 correlation) with ISCCP low- plus middle-cloud cover. The monthly averaged diurnal variations in cloud fraction show marked seasonal variability in the amplitude and temporal structure. The mean annual cloud fraction is lower than the mean annual nighttime cloud fraction by about 9%. Annual and diurnal cycles of surface longwave and shortwave cloud radiative forcing were also estimated. The longwave cloud radiative forcing is about 45 W m−2 year-round, but, because of highly negative shortwave cloud radiative forcing, the net cloud radiative forcing is always negative with an annual mean of −50 W m−2.This research was supported by the Climate Prediction Program for the Americas (CPPA) of NOAA’s Climate Program Office. The Stratus Ocean Reference Station at 20°S, 85°W is supported by NOAA’s Climate Observation Program

Clear-sky biases in satellite infrared estimates of upper tropospheric humidity and its trends

We use microwave retrievals of upper tropospheric humidity (UTH) to estimate the impact of clear-sky-only sampling by infrared instruments on the distribution, variability and trends in UTH. Our method isolates the impact of the clear-sky-only sampling, without convolving errors from other sources. On daily time scales IR-sampled UTH contains large data gaps in convectively active areas, with only about 20-30 % of the tropics (30 S­ 30 N) being sampled. This results in a dry bias of about -9 %RH in the area-weighted tropical daily UTH time series. On monthly scales, maximum clear-sky bias (CSB) is up to -30 %RH over convectively active areas. The magnitude of CSB shows significant correlations with UTH itself (-0.5) and also with the variability in UTH (-0.6). We also show that IR-sampled UTH time series have higher interannual variability and smaller trends compared to microwave sampling. We argue that a significant part of the smaller trend results from the contrasting influence of diurnal drift in the satellite measurements on the wet and dry regions of the tropics

Understanding climate feedbacks with idealized models

The global mean surface air temperature change in response to global warming, namely climate sensitivity, plays a central role in climate change studies, and the estimates of climate sensitivity depend critically on the climate feedbacks, the processes that can either amplify or dampen the responses of the climate system to external perturbations. The goal of this thesis is to understand climate feedbacks through idealized climate models. The first part explores the roles of climate feedbacks in polar amplification of surface temperature change. By running idealized aquaplanet simulations with a hierarchy of radiation schemes (without sea ice and clouds), and by decomposing the total surface temperature responses into different components through the radiative kernel method, we find the poleward heat transport, the lapse rate and Planck feedbacks contribute to amplified surface temperature changes in the polar region, while the forcing and water vapor feedback dominates the tropical temperature change. The second part investigates the underlying causes of cloud feedback uncertainty with a simple cloud scheme. The scheme diagnoses the cloud fraction from relative humidity and other variables such as inversion strength, and its optical properties such as effective radius and cloud water content are prescribed as simple functions of temperature. The simulations show this scheme can capture the basic feature of cloud climatology. Through a series of perturbed parameter ensemble global warming simulations, part of the inter-model spread of cloud feedbacks among general circulation models can be reproduced. In addition, the low cloud amount feedback, especially over the low-latitude subsidence regions, is the largest contributor to the net cloud feedback uncertainty. The cloud controlling factor analysis suggests that the sea surface temperature (SST) and estimated inversion strength (EIS) have opposite impacts on marine low cloud amounts, but their responses to SST rather than EIS seem to bring larger uncertainty. Finally, the equilibrium climate sensitivity and cloud feedback over tropical subsidence regimes show a robust linear relationship, implying a possible constraint for climate sensitivity

Interactions of arctic clouds, radiation, and sea ice in present-day and future climates

2016 Spring.Includes bibliographical references.The Arctic climate system involves complex interactions among the atmosphere, land surface, and the sea-ice-covered Arctic Ocean. Observed changes in the Arctic have emerged and projected climate trends are of significant concern. Surface warming over the last few decades is nearly double that of the entire Earth. Reduced sea-ice extent and volume, changes to ecosystems, and melting permafrost are some examples of noticeable changes in the region. This work is aimed at improving our understanding of how Arctic clouds interact with, and influence, the surface budget, how clouds influence the distribution of sea ice, and the role of downwelling longwave radiation (DLR) in climate change. In the first half of this study, we explore the roles of sea-ice thickness and downwelling longwave radiation in Arctic amplification. As the Arctic sea ice thins and ultimately disappears in a warming climate, its insulating power decreases. This causes the surface air temperature to approach the temperature of the relatively warm ocean water below the ice. The resulting increases in air temperature, water vapor and cloudiness lead to an increase in the surface downwelling longwave radiation, which enables a further thinning of the ice. This positive ice-insulation feedback operates mainly in the autumn and winter. A climate-change simulation with the Community Earth System Model shows that, averaged over the year, the increase in Arctic DLR is three times stronger than the increase in Arctic absorbed solar radiation at the surface. The warming of the surface air over the Arctic Ocean during fall and winter creates a strong thermal contrast with the colder surrounding continents. Sea-level pressure falls over the Arctic Ocean and the high-latitude circulation reorganizes into a shallow "winter monsoon." The resulting increase in surface wind speed promotes stronger surface evaporation and higher humidity over portions of the Arctic Ocean, thus reinforcing the ice-insulation feedback. In the second half of this study, we explore the effects of super-parameterization on the Arctic climate by evaluating a number of key atmospheric characteristics that strongly influence the regional and global climate. One aspect in particular that we examine is the occurrence of Arctic weather states. Observations show that during winter the Arctic exhibits two preferred and persistent states — a radiatively clear and an opaquely cloudy state. These distinct regimes are influenced by the phase of the clouds and affect the surface radiative fluxes. We explore the radiative and microphysical effects of these Arctic clouds and the influence on these regimes in two present-day climate simulations. We compare simulations performed with the Community Earth System Model, and its super-parameterized counterpart (SP-CESM). We find that the SP-CESM is able to better reproduce both of the preferred winter states, compared to CESM, and has an overall more realistic representation of the Arctic climate

地表面放射収支とその変動要因に関する研究

Tohoku University早坂忠裕課