The recent Arctic sea ice loss is a key driver of the amplified surface warming in the northern high latitudes, and simultaneously a major source of u…The recent Arctic sea ice loss is a key driver of the amplified surface warming in the northern high latitudes, and simultaneously a major source of uncertainty in model projections of Arctic climate change. Previous work has shown that the spread in model predictions of future Arctic amplification (AA) can be traced back to the inter-model spread in simulated long-term sea ice loss. We demonstrate that the strength of future AA is further linked to the current climate’s, observable sea ice state across the multi-model ensemble of the 6th Coupled Model Intercomparison Project (CMIP6). The implication is that the sea-ice climatology sets the stage for long-term changes through the 21st century, which mediate the degree by which Arctic warming is amplified with respect to global warming. We determine that a lower base-climate sea ice extent and sea ice concentration (SIC) in CMIP6 models enable stronger ice melt in both future climate and during the seasonal cycle. In particular, models with lower Arctic-mean SIC project stronger future ice loss and a more intense seasonal cycle in ice melt and growth. Both processes systemically link to a larger future AA across climate models. These results are manifested by the role of climate feedbacks that have been widely identified as major drivers of AA. We show in particular that models with low base-climate SIC predict a systematically stronger warming contribution through both sea-ice albedo feedback and temperature feedbacks in the future, as compared to models with high SIC. From our derived linear regressions in conjunction with observations, we estimate a 21st-century AA over sea ice of 2.47–3.34 with respect to global warming. Lastly, from the tight relationship between base-climate SIC and the projected timing of an ice-free September, we predict a seasonally ice-free Arctic by mid-century under a high-emission scenario.more
The broad geographical coverage and high temporal and spatial resolution of geostationary satellite data provide an excellent opportunity to collect i…The broad geographical coverage and high temporal and spatial resolution of geostationary satellite data provide an excellent opportunity to collect information on variables whose spatial distribution and temporal variability are not adequately represented by in situ networks. This study focuses on assessing the effectiveness of two geostationary satellite-based sunshine duration (SDU) datasets over Brazil, given the relevance of SDU to various fields, such as agriculture and the energy sector, to ensure reliable SDU data over the country. The analyzed datasets are the operational products provided by the Satellite Application Facility on Climate Monitoring (CMSAF) that uses data achieved with the Meteorological Satellite (Meteosat) series and by the Satellite and Meteorological Sensors Division of the National Institute for Space Research (DISSM–INPE) that employs Geostationary Operational Environmental Satellite (GOES) data. The analyzed period ranges from September 2013 to December 2017. The mean bias error (MBE), mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (r), and scatterplots between satellite products and in situ daily SDU measurements provided by the National Institute of Meteorology (INMET) were used to access the performance of the products. They were calculated on a monthly basis and grouped into climate regions. The statistical parameters exhibited a uniform spatial distribution, indicating homogeneity within a given region. Except for the tropical northeast oriental (TNO) region, there were no significant seasonal dependencies observed. The MBE values for both satellite products were generally low across most regions in Brazil, mainly between 0 and 1 h. The correlation coefficient (r) results indicated a strong agreement between the estimated values and the observed data, with an overall r value exceeding 0.8. Nevertheless, there were notable discrepancies in specific areas. The CMSAF product showed a tendency to overestimate observations in the TNO region, with the MBE consistently exceeding 1 h for all months, while the DISSM product exhibited a negative gradient of the MBE values in the west–east direction in the northern portion of Brazil. The scatterplots for the TNO region revealed that the underestimation pattern observed in the DISSM product was influenced by the sky condition, with more accurate estimations observed under cloudy skies. Additional analysis suggested that the biases observed might be attributed to the misrepresentation of clear-sky reflectance. In the case of the CMSAF product, the overestimation tendency observed in the TNO region appeared to be a result of systematic underestimation of the effective cloud albedo. The findings indicated that both satellite-based SDU products generally exhibited good agreement with the ground observations across Brazil, although their performance varied across different regions and seasons. The analyzed operational satellite products present a reliable source of data to several applications, which is an asset due to its high spatial resolution and low time latency.more
Grid datasets of sunshine duration at high spatial resolution and extending over many decades are required for quantitative applications in regional c…Grid datasets of sunshine duration at high spatial resolution and extending over many decades are required for quantitative applications in regional climatology and environmental change (e.g., modelling of droughts and snow/ice covers, evaluation of clouds in numerical models, mapping of solar energy potentials). We present a new gridded dataset of relative (and derived absolute) sunshine duration for Austria at a grid spacing of 1 km, extending back until 1961 at daily time resolution. Challenges in the dataset construction were consistency issues in the available station data, the scarcity of long time series, and the high variation of cloudiness in the study region. The challenges were addressed by special efforts to correct evident breaks in the station series and by adopting an analysis method, which combines station data with satellite data. The methodology merges the data sources non-contemporaneously, using statistical patterns distilled over a short period, which allowed involving satellite data even for the early part of the study period. The resulting fields contain plausible mesoscale structures, which could not be resolved by the station network alone. On average, the analyses explain 47% of the spatial variance in daily sunshine duration at the stations. Evaluation revealed a slight systematic underestimation (− 1.5%) and a mean absolute error of 9.2%. The average error is larger during winter, at high altitudes, and around the 1990s. The dataset exhibits a conditional bias, which can lead to considerable systematic errors (up to 15%) when calculating sunshine-related climate indices.more
Forty years of cloud observations are available globally from satellites, allowing derivation of climate data records (CDRs) for climate change studie…Forty years of cloud observations are available globally from satellites, allowing derivation of climate data records (CDRs) for climate change studies. The aim of this study is to investigate how stable these cloud CDRs are and whether they qualify stability requirements recommended by the WMO’s Global Climate Observing System (GCOS). We also investigate robust trends in global total cloud amount (CA) and cloud top temperature (CTT) that are significant and common across all CDRs. The latest versions of four global cloud CDRs, namely CLARA-A3, ESA Cloud CCI, PATMOS-x, and ISCCP-HGM are analysed. This assessment finds that all three AVHRR-based cloud CDRs (i.e., CLARA-A3, ESA Cloud CCI and PATMOS-x) satisfy even the strictest GCOS stability requirements for CA and CTT when averaged globally. While CLARA-A3 is most stable in global averages when tested against MODIS-Aqua, PATMOS-x offers the most stable CDR spatially. While we find these results highly encouraging, there remain, however, large spatial differences in the stability of and across the CDRs. All four CDRs continue to agree on the statistically significant decrease in global cloud amount over the last four decades, although this decrease is now weaker compared to the previous assessments. This decreasing trend has been stabilizing or even reversing in the last two decades; the latter is seen also in MODIS-Aqua and CALIPSO GEWEX datasets. Statistically significant trends in CTT are observed in global averages in the AVHRR-based CDRs, but the spatial agreement in the sign and the magnitude of the trends is weaker compared to those in CA. We also present maps of Common Stability Coverage and Common Trend Coverage that could provide a valuable metric to carry out an ensemble-based analysis of the CDRs.more
Accurate solar resource forecasting remains a challenge. Electricity grid applications require both days-ahead and intra-day prediction. Satellite-bas…Accurate solar resource forecasting remains a challenge. Electricity grid applications require both days-ahead and intra-day prediction. Satellite-based methods are known to be the best option for hourly intra-day solar forecasts up to some hours ahead. An adapted Deep Learning (DL) method has been recently reported to outperform the traditional Cloud Motion Vectors (CMV) strategy. This article analyzes the utilization of a well-documented computer vision DL architecture, the U-Net in various forms, for the satellite Earth albedo forecast problem (cloudiness), a straightforward proxy for solar irradiance forecast. It is shown that the U -Net performs better than advanced and optimized CMV techniques and previous art IrradianceNet, setting it at the state-of-the-art. The tests are done over the Pampa Humeda region of southeast South America, an area in which challenging cloud conditions are frequent. The data for this study are GOES-16 visible channel images. These images present a finer spatial (& SIME; 1 km/pixel) and temporal (10 min) resolution than previously explored data sources for solar forecasting. Moreover, the image size used here is x4 bigger (1024 x 1024 pixels) and the predictions reach further into the future (5 h) than in previous works. The analysis includes several ablation studies, involving different architectures, optimization objectives, inputs, and network sizes. The U-Net is optimized for direct and differential image prediction, being the latter a better-performing option. More notably, the U-Net models are shown to be able to predict cloud extinction, something that has been a barrier for CMV methods.more
With this study, 12 empirical models in the literature, 2 new models developed within the scope of this study, SARAH and CMSAF satellite-based models,…With this study, 12 empirical models in the literature, 2 new models developed within the scope of this study, SARAH and CMSAF satellite-based models, COSMO and ERA5 re-analysis solar radiation data sets in the PVGIS database were compared in order to detect the monthly average global solar radiation coming to the horizontal plane of Usak province. New models developed within the scope of the study; it uses the region's temperature, cloudiness coefficient and sunset hour angle. In comparison of the datas within the scope of the study; coefficient of determination (R²), mean percent error (MPE), deviation error (MBE), root mean square error (RMSE), absolute relative error (ARE) parameters were used. As a result of the evaluations, the method that most successfully predicts the global solar radiation values of Usak province was tried to be determined. According to the monthly evaluation of the models; It was determined that 14 models and satellite-based systems have absolute relative error values below 5% in March-April-May-June, September-October and December. The most accurate estimates were realized for May in 16 of 18 different estimation methods used in the study. The coefficient of determination of empirical models and PVGIS data sets was above 0.97. When the success of the models was evaluated according to the RMSE values, It was determined that the logarithmic based Model 14 (0.90058 RMSE, 0.98327 R2, -1.079894 MPE, -0.05033 MBE, 0.185628 t) which was obtained by using the sunset hour angle and the max-min temperature difference developed within the scope of this study, made the most accurate estimations. COSMO data from spatial data (1.053134 RMSE, 0.979036 R2, -1.196348 MPE, -0.25105 MBE, 0.8141 t) made successful estimations, but the accuracy of the COSMO data was lower than the data estimated by Model 14. It was concluded that used the models and satellite-based systems were generally successful. As a result, In the studies to be carried out for the global solar radiation forecast of Usak province. It has been concluded that Model 14 developed within the scope of the study can be used in precise calculations and COSMO data from PVGIS datas can be used in more superficial or pre-feasibility studies.more
To improve Arctic sea ice simulations by the First Institute of Oceanography-Earth System Model (FIO-ESM), the model version has been updated from FIO…To improve Arctic sea ice simulations by the First Institute of Oceanography-Earth System Model (FIO-ESM), the model version has been updated from FIO-ESM v2.0 to FIO-ESM v2.1 by upgrading its sea ice component from Los Alamos Sea-Ice Model (CICE) version 4.0 (CICE4.0) to CICE6.0, and improving the ice-ocean heat exchange process from a two-equation boundary condition parameterization to a more realistic three-equation boundary condition parameterization. Numerical experiments show that the underestimation of Arctic summer sea ice extent (SIE) in FIO-ESM v2.0 is significantly improved by the model enhancements. The root mean square error of the simulated Arctic September SIE during 1979-2014 is reduced from 2.9 million to 0.7 million km2. Nevertheless, the biases of Antarctic SIE increase following the model version update. FIO-ESM v2.1 performs well for the simulations of surface air temperature, sea surface temperature, Atlantic Meridional Overturning Circulation, and Arctic SIE; however, it overestimates summer SIE in the Antarctic. Furthermore, future projections based on FIO-ESM v2.1 indicate that the first ice-free Arctic summer will occur in the 2050s and the 2040s under SSP2-4.5 and SSP5-8.5, respectively.more
Abstract
The spectral long-wave feedback parameter represents how Earth’s outgoing long-wave radiation adjusts to temperature changes and …Abstract
The spectral long-wave feedback parameter represents how Earth’s outgoing long-wave radiation adjusts to temperature changes and directly impacts Earth’s climate sensitivity. Most research so far has focused on the spectral integral of the feedback parameter. Spectrally resolving the feedback parameter permits inferring information about the vertical distribution of long-wave feedbacks, thus gaining a better understanding of the underlying processes. However, investigations of the spectral long-wave feedback parameter have so far been limited mostly to model studies. Here we show that it is possible to directly observe the global mean all-sky spectral long-wave feedback parameter using satellite observations of seasonal and interannual variability. We find that spectral bands subject to strong water-vapour absorption exhibit a substantial stabilizing net feedback. We demonstrate that part of this stabilizing feedback is caused by the change of relative humidity with warming, the radiative fingerprints of which can be directly observed. Therefore, our findings emphasize the importance of better understanding processes affecting the present distribution and future trends in relative humidity. This observational constraint on the spectral long-wave feedback parameter can be used to evaluate the representation of long-wave feedbacks in global climate models and to better constrain Earth’s climate sensitivity.more