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The article examines the possible reasons for the severe drought in Northeast Asia in summer of 2014. Topics covered include the influence of the meridional propagation of the Mei-Yu front on the pattern of summer precipitation, the effect of greenhouse gas emissions and anthropogenic activities on increased summer temperature and the drought in northern China and flooding in the south.

Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model's ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty. [ABSTRACT FROM AUTHOR]

The article discusses the integration of climate risk information in development planning as various sectors and countries are preparing for climate change impacts. It highlights information in which climate risk information can be used in adaptation assessments. It also describes knowledge gaps and opportunities for improving the uptake of climate risk information for the year 2020.

Analysis of variance (ANOVA) is a powerful statistical technique for making inferences about experiments that are influenced by multiple factors. Whilst common in many other scientific fields, its use within the climate community has been limited to date. Here we review the basis for ANOVA and how, in particular, it can be applied to partition the variance in a multi-model ensemble of Atmospheric General Circulation Model simulations. We examine an ensemble of four AGCMs forced with observed twentieth century sea surface temperatures (SST). We show that the dominant contributions to the total variance of seasonal mean sea level pressure arise from between-model differences (the bias term) and internal noise (the noise term). However, which term is most important varies from region to region. Of particular interest is the interaction term, which describes differences between the models in their responses to common SST forcing. The interaction term is found to be largest over the Indian Ocean (in all seasons), and over the subtropical Northwest Pacific in boreal summer. The differences between the model responses in these regions suggest differences in their simulation of atmospheric teleconnections, with potentially important implications, e.g. for seasonal predictions of the South and East Asian Monsoons. Examination of these differences may lead to an understanding of the reasons why models respond differently to common forcing, and ultimately to improvements in the performance of climate models. [ABSTRACT FROM AUTHOR]

Three interrelated climate phenomena are at the center of the Climate Variability and Predictability (CLIVAR) Atlantic research: tropical Atlantic variability (TAV), the North Atlantic Oscillation (NAO), and the Atlantic meridional overturning circulation (MOC). These phenomena produce a myriad of impacts on society and the environment on seasonal, interannual, and longer time scales through variability manifest as coherent fluctuations in ocean and land temperature, rainfall, and extreme events. Improved understanding of this variability is essential for assessing the likely range of future climate fluctuations and the extent to which they may be predictable, as well as understanding the potential impact of human-induced climate change. CLIVAR is addressing these issues through prioritized and integrated plans for short-term and sustained observations, basin-scale reanalysis, and modeling and theoretical investigations of the coupled Atlantic climate system and its links to remote regions. In this paper, a brief review of the state of understanding of Atlantic climate variability and achievements to date is provided. Considerable discussion is given to future challenges related to building and sustaining observing systems, developing synthesis strategies to support understanding and attribution of observed change, understanding sources of predictability, and developing prediction systems in order to meet the scientific objectives of the CLIVAR Atlantic program. [ABSTRACT FROM AUTHOR]

The surface heat flux feedback is estimated in the Atlantic and the extra-tropical Indo-Pacific, using monthly heat flux and sea surface temperature anomaly data from control simulations with five global climate models, and it is compared to estimates derived from COADS and the NCEP reanalysis. In all data sets, the heat flux feedback is negative nearly everywhere and damps the sea surface temperature anomalies. At extra-tropical latitudes, it is strongly dominated by the turbulent fluxes. The radiative feedback can be positive or negative, depending on location and season, but it remains small, except in some models in the tropical Atlantic. The negative heat flux feedback is strong in the mid-latitude storm tracks, exceeding 40 W m–2 K–1 at place, but in the Northern Hemisphere it is substantially underestimated in several models. The negative feedback weakens at high latitudes, although the models do not reproduce the weak positive feedback found in NCEP in the northern North Atlantic. The main differences are found in the tropical Atlantic where the heat flux feedback is weakly negative in some models , as in the observations, and strongly negative in others where it can exceed 30 W m–2 K–1 at large scales, in part because of a strong contribution of the radiative fluxes, in particular during spring. A comparison between models with similar atmospheric or oceanic components suggests that the atmospheric model is primarily responsible for the heat flux feedback differences at extra-tropical latitudes. In the tropical Atlantic, the ocean behavior plays an equal role. The differences in heat flux feedback in the tropical Atlantic are reflected in the sea surface temperature anomaly persistence, which is too small in models where the heat flux damping is large. A good representation of the heat flux feedback is thus required to simulate climate variability realistically. [ABSTRACT FROM AUTHOR]

A coupled ocean–atmosphere general circulation model has been used to study the variations of North Atlantic upper ocean heat content (OHC), sea surface temperature (SST) and ocean heat transport (OHT), and the relationships between these three quantities. We find that OHC anomalies, and salinity anomalies, propagate anti-cyclonically around the North Atlantic subtropical gyre. They propagate eastward in midlatitudes and westward in low latitudes. Both the advection of mean temperature by anomalous currents and the advection of temperature anomalies by mean currents are responsible for these zonal propagations. In addition to zonal propagations, upper ocean temperature anomalies propagate southward in the eastern North Atlantic, where subduction plays a dominant role. Variability in the northward OHT in the Atlantic is primarily governed by variability in the ocean circulation rather than variability in temperatures. Fluctuations in OHT are the major cause of anomalies in OHC and SST in the Gulf Stream extension region. This is true both for interannual variability and for decadal variability. On interannual time scales, however, surface fluxes also make a significant contribution. Analysis of the relationships of OHT with OHC and SST suggests that a knowledge of OHT fluctuations could be used to predict variations in OHC, and therefore sea surface temperatures, several years in advance. [ABSTRACT FROM AUTHOR]

This article presents a reply to comments on an article about atmospheric general circulation model study of potential climate predictability over the North Atlantic European region. The critic presents an analysis of predictability inferred from the results of a coupled forecasting system, which uses a coupled model with an atmosphere component essentially identical to that used in the study. The authors welcome the analysis of predictability in a current state-of-the-art coupled forecasting system. However, they disagree concerning the implications of the negative results.