Improved seasonal climate forecasts for the Caribbean region using the Florida State University Synthetic Superensemble

Summary Climate variations in the Caribbean, largely manifest in rainfall activity, have important consequences for the large-scale water budget, natural vegetation, and land use in the region. The wet and dry seasons will be defined, and the important roles played by the El Ni?o-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) in modulating the rainfall during these seasons will be discussed. The seasonal climate forecasts in this paper are made by 13 state of the art coupled atmosphere-ocean general circulation models (CGCMs) and by the Florida State University Synthetic Superensemble (FSUSSE), whose forecasts are obtained by a weighted combination of the individual CGCM forecasts based on a training period. The success of the models in simulating the observed 1989–2001 climatology of the various forecast parameters will be examined and linked to the models’ success in predicting the seasonal climate for individual years. Seasonal forecasts will be examined for precipitation, sea-surface temperature (SST), 2-meter air temperature, and 850 hPa u- and v-wind components during the period 1989–2001. Evaluation metrics include root mean square (RMS) error and Brier skill score. It will be shown that the FSUSSE is superior to the individual CGCMs and their ensemble mean both in simulating the 1989–2001 climatology for the various parameters and in predicting the seasonal climate of the various parameters for individual years. The seasonal climate forecasts of the FSUSSE and of the ensemble mean of the 13 state of the art CGCMs will be evaluated for years (during the period 1989–2001) that have particular ENSO and NAO signals that are known to influence Caribbean weather, particularly the rainfall. It will be shown that the FSUSSE provides superior forecasts of rainfall, SST, 2-meter air temperature, and 850 hPa u- and v-wind components during dry summers that are modulated by negative SOI and/or positive NAO indices. Such summers have become a feature of a twenty-year pattern of drought in the Caribbean region. The results presented in this paper will show that the FSUSSE is a valuable tool for forecasting rainfall and other atmospheric and oceanic variables during such periods of drought.     

Dynamical seasonal prediction using the global environmental multiscale model with a variable resolution modeling approach

In this work we evaluate seasonal forecasts performed with the global environmental multiscale model (GEM) using a variable resolution approach and with a high-resolution region over different geographical locations. Therefore, using two grid positions, one over North America and the other over the tropical Pacific-eastern Indian Ocean, we compare the seasonal predictions performed with the variable resolution approach with seasonal forecast performed with the uniform grid GEM model. For each model configuration, a ten-member ensemble forecast of 4?months is performed starting from the first of December of selected ENSO winters between 1982 and 2000. The sea surface temperature anomaly of the month preceding the forecast (November) is persisted throughout the forecast period. There is not enough evidence to indicate that a Stretch-Grid configuration has a clear advantage in seasonal prediction compared to a Uniform-Grid configuration. Forecasts with highly resolved grids placed over North America have more accurate seasonal mean anomalies and more skill in representing near surface temperature over the North American continent. For 500-hPa geopotential height, however, no configuration stands out to be consistently superior in forecasting the ENSO related seasonal mean anomalies and skill score.     

Equity,environmental justice and sustainability: incomplete approaches in climate change politics

The diversity of the different takes on the ethical concepts of equity and environmental justice in the environmental literature requires that a unifying framework, to properly map their theoretical and conceptual loci in environmental ethics, is defined. Specifically, the concepts of equity and environmental justice have often been conflated into one even though they rest on different philosophical foundations and have different denotations, connotations and implications. The inconsistent use of these constructs is perverse in both scholarly discussions and global environmental policy formulation and detracts from conceptual clarity and their analytical usefulness. The aim of this paper, therefore, is to provide a mapping of the different takes to the moral aspects of global environmental decisions. Attempt is made to clarify the conceptual and philosophical denotations of environmental justice and equity from the point of view of philosophy, law and moral ethics. Our analysis leads to the construing of environmental justice as a broad overarching concept encompassing all justice issues in environmental decision-making, including both procedural and distributive justice, which is what is usually meant by equity. The resulting framework allows us to grasp the competing, conflicting and incomplete approaches to environmental justice as captured in the North–South conceptions of the construct more effectively. It is our hope that our conclusions would be valuable to researchers of global environmental change and politics.     

Statistical adjustment,calibration and downscaling of seasonal forecasts: a case-study for Southeast Asia

Climate Dynamics - The present paper is a follow-on of the work presented in Manzanas et al. (Clim Dyn 53(3–4):1287–1305, 2019) which provides a comprehensive intercomparison...     

Relevance of soil moisture for seasonal climate predictions: a preliminary study

In the framework of the Global Soil Wetness Project (GSWP), the ISBA land-surface scheme of the ARPEGE atmospheric general circulation model has been forced with meteorological observations and analyses in order to produce a two-year (1987–1988) soil moisture climatology at a 1^°×1^° horizontal resolution. This climatology is model dependent, but it is the climatology that the ARPEGE model would produce if its precipitation and radiative fluxes were perfectly simulated. In the present study, ensembles of seasonal simulations (March to September) have been performed for 1987 and 1988, in which the total soil water content simulated by ARPEGE is relaxed towards the GSWP climatology. The results indicate that the relaxation has a positive impact on both the model's climatology and the simulated interannual variability, thereby confirming the utility of the GSWP soil moisture data for prescribing initial or boundary conditions in comprehensive climate and numerical weather prediction models. They also demonstrate the relevance of soil moisture for achieving realistic simulations of the Northern Hemisphere summer climate. In order to get closer to the framework of seasonal predictions, additional experiments have been performed in which GSWP is only used for initialising soil moisture at the beginning of the summer season (the relaxation towards GSWP is removed on 1st June). The results show a limited improvement of the interannual variability, compared to the simulations initialised from the ARPEGE climatology. However, some regional patterns of the precipitation differences between 1987 and 1988 are better captured, suggesting that seasonal predictions can benefit from a better initialisation of soil moisture.     

Nitrogen pollution: a key building block for addressing climate change

The current national commitments under the Paris Climate Agreement fall short of what is needed to stay below a 2 °C increase in global average temperature. One approach that has been proposed to close this ambition gap is the building blocks strategy, which aims to encourage initiatives focused on non-climate actions that can deliver a climate benefit. A key option under this framework is reducing global nitrogen pollution. Nitrogen pollution—driven largely by the inefficient use of synthetic fertilizer and manure—is one of the most important environmental issues of the twenty-first century, not least because of its climate impacts. Ambitiously mitigating nitrogen pollution could avoid greenhouse gas emissions equivalent to 5–10% of the remaining allowable emissions consistent with the 2 °C target. However, the climate benefits would be a minor component of the overall environmental benefits of reducing nitrogen pollution, which would come mainly from avoided water and air pollution. The fact that these benefits would accrue mostly at local scales is especially important for countries like the United States, marked by a shift toward “economic nationalism.” In these countries, the most politically viable climate actions will likely be ones that produce local benefits as great, if not greater, than those achieved internationally. This is also likely to be true in countries like China, where local nitrogen-related issues such as air and water pollution remain major national priorities. Nevertheless, there are several challenges that could stand in the way of improved nitrogen management being a successful building block: integrated nitrogen management solutions that reduce the risk of pollution swapping need to be developed, the policy challenges related to changing and monitoring farmer behavior need to be addressed, and nitrogen’s role as an essential agricultural input needs to be respected. A better understanding of these challenges could also help policy-makers develop viable climate mitigation strategies across the entire agricultural sector.     

Citizens, consumers and sustainability: (Re)Framing environmental practice in an age of climate change

Recent moves by national and local policy makers have sought to encourage individuals to engage in a wide range of pro-environmental practices to address both discrete environmental problems and major, global challenges such as climate change. The major framing device for these developments is the notion of ‘citizen–consumers’, which positions individual ecological responsibilities alongside consumer choice logics in a Neo-liberal socio-economic framework. In the environmental social sciences, there have been recent moves to interpret the citizen–consumer through adopting a social practices approach, which advances the notion that in understanding environmental commitments, a deeper appreciation of underlying norms, values, identity politics and consumption is required to uncover the complex processes that lead to environmental practices in specific contexts. This paper argues that whilst these approaches have considerable utility in tracing the normalisation of established and discrete environmental practices in particular contexts, the issue of climate change represents an independent and over-arching discursive conflict between new and embedded practices that challenges the ability of citizen–consumers to act as agents for change. Accordingly, the data presented in this paper suggest that climate change can be seen as an unsettling and dynamic issue that generates discursive conflict in its own right around fundamental issues of knowledge, responsibility, scale and place. The paper therefore argues that a new and more critical perspective is required within environmental social science to understand (conflicting) discourses of sustainable living between the ‘passive’ normalisation of conventional environmental practice and the ‘contested’ ambiguities of climate change.     

基于GFS预报资料的强对流相似预报研究

本文基于数值模式预报发展了强对流相似预报方法。该方法采用对流参数作为相似预报量,对比当前预报与历史预报的相似物理量场挑选与当前预报最相似的时刻,并利用这些时刻对应的强对流观测权重叠加作为当前预报时刻的强对流预报。以NCEP/GFS预报场资料,对该方法的有效性进行了检验。检验结果表明,该方法对中国地区的强对流有良好预报效果。作为数值预报的有效补充,该方法具有以下几点优势:简便易实现,具有很高的可移植性:以对流参数作为相似预报量,充分利用了模式对温,压,湿,风等基本气象要素预报比较准确的优势;有效避免了对流参数进行强对流预报时的临界值选取问题,考虑了强对流发生的地域性,气候性特点。     

Multi-Year Simulations and Experimental Seasonal Predictions for Rainy Seasons in China by Using a Nested Regional Climate Model （RegCM_NCC） Part Ⅱ： The Experimental Seasonal Prediction

A nested regional climate model has been experimentally used in the seasonal prediction at the China National Climate Center (NCC) since 2001. The NCC/IAP (Institute of Atmospheric Physics) T63 coupled GCM (CGCM) provides the boundary and initial conditions for driving the regional climate model (RegCM NCC). The latter has a 60-km horizontal resolution and improved physical parameterization schemes including the mass flux cumulus parameterization scheme, the turbulent kinetic energy closure scheme (TKE) and an improved land process model (LPM). The large-scale terrain features such as the Tibetan Plateau are included in the larger domain to produce the topographic forcing on the rain-producing systems. A sensitivity study of the East Asian climate with regard to the above physical processes has been presented in the first part of the present paper. This is the second part, as a continuation of Part I. In order to verify the performance of the nested regional climate model, a ten-year simulation driven by NCEP reanalysis datasets has been made to explore the performance of the East Asian climate simulation and to identify the model’s systematic errors. At the same time, comparative simulation experiments for 5 years between the RegCM2 and RegCM NCC have been done to further understand their differences in simulation performance. Also, a ten-year hindcast (1991–2000) for summer (June–August), the rainy season in China, has been undertaken. The preliminary results have shown that the RegCM NCC is capable of predicting the major seasonal rain belts. The best predicted regions with high anomaly correlation coefficient (ACC) are located in the eastern part of West China, in Northeast China and in North China, where the CGCM has maximum prediction skill as well. This fact may reflect the importance of the largescale forcing. One significant improvement of the prediction derived from RegCM NCC is the increase of ACC in the Yangtze River valley where the CGCM has a very low, even a negative, ACC. The reason behind this improvement is likely to be related to the more realistic representation of the large-scale terrain features of the Tibetan Plateau. Presumably, many rain-producing systems may be generated over or near the Tibetan Plateau and may then move eastward along the Yangtze River basin steered by upper-level westerly airflow, thus leading to enhancement of rainfalls in the mid and lower basins of the Yangtze River. The real-time experimental predictions for summer in 2001, 2002, 2003 and 2004 by using this nested RegCM NCC were made. The results are basically reasonable compared with the observations.     

U-Net: A deep-learning method for improving summer precipitation forecasts in China

A deep-learning method named U-Net was applied to improve the skill in forecasting summer (June–August) precipitation for at a one-month lead during the period 1981–2020 in China. The variables of geopotential height, soil moisture, sea level pressure, sea surface temperature, ocean salinity, and snow were considered as the model input to revise the seasonal prediction of the Climate Forecast System, version 2 (CFSv2). Results showed that on average U-Net reduced the root-mean-square error of the original CFSv2 prediction by 49.7% and 42.7% for the validation and testing set, respectively. The most improved areas were Northwest, Southwest, and Southeast China. The anomaly same sign percentages and temporal and spatial correlation coefficients did not present significant improvement but maintained the comparable performances of CFSv2. Sensitivity experiments showed that soil moisture is the most crucial factor in predicting summer rainfall in China, followed by geopotential height. Due to its advantages in handling small training dataset sizes, U-Net is a promising deep-learning method for seasonal rainfall prediction.摘要本研究应用了名为U-Net的深度学习方法来提高中国夏季 (6–8月) 降水的预报技能, 预报时段为1981–2020年, 预报提前期为一个月. 将位势高度场, 土壤湿度, 海平面气压, 海表面温度, 海洋盐度和青藏高原积雪等变量作为模型输入, 本文对美国NCAR气候预报系统第2版 (CFSv2) 的季节性预报结果进行了修正. 结果显示, 在验证集和测试集上, U-Net平均将原CFSv2预测的均方根误差分别减少了49.7%和42.7%. 预报结果改善最大的地区是中国的西北,西南和东南地区. 然而, 同号率和时空相关系数没有得到明显改善, 但仍与CFSv2的预测技巧持平. 敏感性实验表明, 土壤湿度是预测中国夏季降雨的最关键因素, 其次是位势高度场. 本研究显示了U-Net模型在训练小样本数据集方面的优势, 为我国汛期季节性降雨预测提供了一种有效的深度学习方法.     

The art of the science: climate forecasts for wildfire management in the southeastern United States

This article illustrates how a wildfire risk forecast evolved iteratively based on stakeholder consultations. An assessment based on phone interviews indicates that such forecasts can assist fire management decisions, such as deployment of human, financial, and material resources and management of forest, timber, and habitats, and public safety. But careful attention to communication, collaboration, and capacity building is key to realizing this potential.     

Linking internal and external transformation for sustainability and climate action: Towards a new research and policy agenda

Climate change is an increasing threat to sustainable development worldwide. However, the dominant incremental policy approaches have not generated action at anywhere near the rate, scale or depth that is needed. This is largely due to the fact that climate change has historically been framed as a purely external, technical challenge. There is an urgent need for a more integral understanding that links internal and external (collective and systems) approaches to support transformation. However, related knowledge is scarce and fragmented across disciplines. This study addresses this gap. Through a systematic literature review, we analyse how the linkages between internal and external change are portrayed and understood in current research. We assess the scope, perspectives and approaches used to understand why, and how, internal change relates to climate action and sustainability. Our results highlight patterns and gaps regarding foci, conceptualisation, methods, epistemology, ontology and ethics that hamper emergent solutions and progress. Starting from the status quo, we propose an integrated model of change as an agenda and roadmap for future research, policy and practice.     

Multi-year Simulations and Experimental Seasonal Predictions for Rainy Seasons in China by Using a Nested Regional Climate Model （RegCM_NCC）. Part Ⅰ： Sensitivity Study

A modified version of the NCAR/RegCM2 has been developed at the National Climate Center （NCC）, China Meteorological Administration, through a series of sensitivity experiments and multi-year simulations and hindcasts, with a special emphasis on the adequate choice of physical parameterization schemes suitable for the East Asian monsoon climate. This regional climate model is nested with the NCC/IAP （Institute of Atmospheric Physics） T63 coupled GCM to make an experimental seasonal prediction for China and East Asia. The four-year （2001 to 2004） prediction results are encouraging. This paper is the first part of a two-part paper, and it mainly describes the sensitivity study of the physical process paraxneterization represented in the model. The systematic errors produced by the different physical parameterization schemes such as the land surface processes, convective precipitation, cloud-radiation transfer process, boundary layer process and large-scale terrain features have been identified based on multi-year and extreme flooding event simulations. A number of comparative experiments has shown that the mass flux scheme （MFS） and Betts-Miller scheme （BM） for convective precipitation, the LPMI （land surface process model I） and LPMII （land surface process model Ⅱ） for the land surface process, the CCM3 radiation transfer scheme for cloud-radiation transfer processes, the TKE （turbulent kinetic energy） scheme for the boundary layer processes and the topography treatment schemes for the Tibetan Plateau are suitable for simulations and prediction of the East Asia monsoon climate in rainy seasons. Based on the above sensitivity study, a modified version of the RegCM2 （RegCM_NCC） has been set up for climate simulations and seasonal predictions.     

Food security in the face of climate change: Adaptive capacity of small-scale social-ecological systems to environmental variability

Improving the adaptive capacity of small-scale irrigation systems to the impacts of climate change is crucial for food security in Asia. This study analyzes the capacity of small-scale irrigation systems dependent on the Asian monsoon to adapt to variability in river discharge caused by climate change. Our study is motivated by the Pumpa irrigation system, a small-scale irrigation system located in Nepal that is a model for this type of system. We developed an agent-based model in which we simulated the decisions farmers make about the irrigation strategy to use according to available water flow. Given the uncertainty associated with how climate change may affect the Asian monsoon, we simulated the performance of the system under different projections of climate change in the region (increase and decrease in rainfall, reduction and expansion of the monsoon season, and changes in the timing of the onset of the monsoon). Accordingly to our simulations, farmers might need to adapt to rainfall intensification and a late onset in the monsoon season. The demands for collective action among farmers (e.g. infrastructure repair, meetings, decisions, etc.) might increase considerably due to climate change. Although our model suggests that investment in new infrastructure might increase the performance of the system under some climate change scenarios, the high inequality among farmers when water availability is reduced might hinder the efficiency of these measures due to a reduction of farmers’ willingness to cooperate. Our modeling exercise helps to hypothesize about the most sensitive climate change scenarios for smallscale irrigation farming in Nepal and helps to frame a discussion of some possible solutions and fundamental trade-offs in the process of adaptation to improve for food and water security under climate change.     

Adaptation of the optimal fingerprint method for climate change detection using a well-conditioned covariance matrix estimate

The “optimal fingerprint” method, usually used for detection and attribution studies, requires to know, or, in practice, to estimate the covariance matrix of the internal climate variability. In this work, a new adaptation of the “optimal fingerprints” method is presented. The main goal is to allow the use of a covariance matrix estimate based on an observation dataset in which the number of years used for covariance estimation is close to the number of observed time series. Our adaptation is based on the use of a regularized estimate of the covariance matrix, that is well-conditioned, and asymptotically more precise, in the sense of the mean square error. This method is shown to be more powerful than the basic “guess pattern fingerprint”, and than the classical use of a pseudo-inverted truncation of the empirical covariance matrix. The construction of the detection test is achieved by using a bootstrap technique particularly well-suited to estimate the internal climate variability in real world observations. In order to validate the efficiency of the detection algorithm with climate data, the methodology presented here is first applied with pseudo-observations derived from transient regional climate change scenarios covering the 1960–2099 period. It is then used to perform a formal detection study of climate change over France, analyzing homogenized observed temperature series from 1900 to 2006. In this case, the estimation of the covariance matrix is only based on a part of the observation dataset. This new approach allows the confirmation and extension of previous results regarding the detection of an anthropogenic climate change signal over the country.     

Validation of precipitation events in a regional climate model simulation using methods from complex systems theory

Summary We show that daily precipitation is characterized by a behaviour regulated by power laws. Both for the frequency distribution of both event intensity and drought duration. In this respect, precipitation appears to follow self-organized criticality laws, much as other geophysical phenomena such as avalanches and earthquakes. We use this feature to validate the simulation of daily precipitation events in a multi-decadal regional climate model experiment for the European region. Our focus is on the Italian peninsula and we show that both the model results and the station observations for daily precipitation intensity and drought length follow power laws with comparable values of the relevant parameters. We suggest that complex systems theory can provide useful tools for the validation of precipitation statistics in climate models.     

Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons

This paper shows demonstrable improvement in the global seasonal climate predictability of boreal summer (at zero lead) and fall (at one season lead) seasonal mean precipitation and surface temperature from a two-tiered seasonal hindcast forced with forecasted SST relative to two other contemporary operational coupled ocean–atmosphere climate models. The results from an extensive set of seasonal hindcasts are analyzed to come to this conclusion. This improvement is attributed to: (1) The multi-model bias corrected SST used to force the atmospheric model. (2) The global atmospheric model which is run at a relatively high resolution of 50 km grid resolution compared to the two other coupled ocean–atmosphere models. (3) The physics of the atmospheric model, especially that related to the convective parameterization scheme. The results of the seasonal hindcast are analyzed for both deterministic and probabilistic skill. The probabilistic skill analysis shows that significant forecast skill can be harvested from these seasonal hindcasts relative to the deterministic skill analysis. The paper concludes that the coupled ocean–atmosphere seasonal hindcasts have reached a reasonable fidelity to exploit their SST anomaly forecasts to force such relatively higher resolution two tier prediction experiments to glean further boreal summer and fall seasonal prediction skill.     

The potential impact of climate change on seasonal snow in New Zealand: part I—an analysis using 12 GCMs

Seasonal snow directly affects New Zealand??s economy through the energy, agriculture and tourism sectors. In New Zealand, little is known about the long-term variability of the snow cover and the expected impacts of climate change on snow cover. The lack of systematic historical snow observations in New Zealand means that information on interannual variability, trends and projections of future seasonal snow must be generated using simulation models. We use a temperature index snow model to calculate the accumulation and ablation of the current (1980?C1999) snowpack for more than 37,000 third-order river basins with 100?m contour intervals, resulting in over 200,000 individual model elements in New Zealand. Using this model, which captures the gross features of snow under the current climate, we assess the range of likely effects of climate change on seasonal snow in New Zealand using downscaled temperature and precipitation changes from the middle of the road (A1B) climate change projections from 12 general circulation models (GCMs). For each of the 12 GCMs, we consider two future time periods 2030?C2049 (mid-point reference 2040) and 2080?C2099 (mid-point reference 2090). These future time periods are compared to simulations of current, 1980?C1999 (mid-point reference 1990), seasonal snow. Our results show that on average at a national scale, at nearly all elevations, the 2040s and 2090s result in a decrease in snow as described by all of our summary statistics: snow duration, percentage of precipitation that is snow and peak snow accumulation in each year. This decrease in snow is more marked at elevations below 1,000?m but is evident at all but the very highest elevations. Relative to snow simulations for average peak snow accumulation for the present, we observe that by the 2040s, depending on the GCM used, there is a reduction of between 3 and 44?% at 1,000?m, and an increase of 8?% through to a reduction of 22?% at 2,000?m. By the 2090s, the average reduction is greater, with a decrease of between 32 and 79?% at 1,000?m and between 6 and 51?% at 2,000?m. More substantial reductions are observed below these elevations. When we consider the elevation where snow duration exceeds 3?months, we see a rise in this elevation from 1,550?m in the 1990s to between 1,550 and 1,750?m by the 2040s and 1,700 and 2,000?m by the 2090s, depending on the GCM used. The results of this work are consistent with our understanding of snow processes in general and with work from other similar mid-latitude locations.     

Sea ice in the Barents Sea: seasonal to interannual variability and climate feedbacks in a global coupled model

Sea ice variability in the Barents Sea and its impact on climate are analyzed using a 465-year control integration of a global coupled atmosphere–ocean–sea ice model. Sensitivity simulations are performed to investigate the response to an isolated sea ice anomaly in the Barents Sea. The interannual variability of sea ice volume in the Barents Sea is mainly determined by variations in sea ice import into Barents Sea from the Central Arctic. This import is primarily driven by the local wind field. Horizontal oceanic heat transport into the Barents Sea is of minor importance for interannual sea ice variations but is important on longer time scales. Events with strong positive sea ice anomalies in the Barents Sea are due to accumulation of sea ice by enhanced sea ice imports and related NAO-like pressure conditions in the years before the event. Sea ice volume and concentration stay above normal in the Barents Sea for about 2 years after an event. This strongly increases the albedo and reduces the ocean heat release to the atmosphere. Consequently, air temperature is much colder than usual in the Barents Sea and surrounding areas. Precipitation is decreased and sea level pressure in the Barents Sea is anomalously high. The large-scale atmospheric response is limited with the main impact being a reduced pressure over Scandinavia in the year after a large ice volume occurs in the Barents Sea. Furthermore, high sea ice volume in the Barents Sea leads to increased sea ice melting and hence reduced surface salinity. Generally, the climate response is smallest in summer and largest in winter and spring.