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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]

This is the second of the two-part paper series on the analysis and evaluation of the Fifth phase of Coupled Model Intercomparison Project (CMIP5) simulation of contemporary climate as well as IPCC, AR5 Representative Concentrations Pathways (RCP), 4.5 and 8.5 scenarios projections of the Greater Horn of Africa (GHA) Climate. In the first part (Otieno and Anyah in Clim Dyn, ) we focused on the historical simulations, whereas this second part primarily focuses on future projections based on the two scenarios. Six Earth System Models (ESMs) from CMIP5 archive have been used to characterize projected changes in seasonal and annual mean precipitation, temperature and the hydrological cycle by the middle of twenty-first century over the GHA region, based on IPCC, 5th Assessment Report (AR5) RCP4.5 and RCP8.5 scenarios. Nearly all the models outputs analyzed reproduce the correct mean annual cycle of precipitation, with some biases among the models in capturing the correct peak of precipitation cycle, more so, March-April-May (MAM) seasonal rainfall over the equatorial GHA region. However, there is significant variation among models in projected precipitation anomalies, with some models projecting an average increase as others project a decrease in precipitation during different seasons. The ensemble mean of the ESMs indicates that the GHA region has been experiencing a steady increase in both precipitation and temperature beginning in the early 1980s and 1970s respectively in both RCP4.5 and RCP8.5 scenarios. Going by the ensemble means, temperatures are projected to steadily increase uniformly in all the seasons at a rate of 0.3/0.5 °C/decade under RCP4.5/8.5 scenarios over northern GHA region leading to an approximate temperature increase of 2/3 °C by the middle of the century. On the other hand, temperatures will likely increase at a rate of 0.3/0.4 °C/decade under RCP4.5/8.5 scenarios in both equatorial and southern GHA region leading to an approximate temperature increase of 2/2.5 °C by the middle of twenty-first century. Nonetheless, projected precipitation increase varied across seasons and sub-regions. With the exception of the equatorial region, that is projected to experience precipitation increase during DJF season, most sub-regions are projected to experience precipitation increase within their peak seasons, with the highest rate of increase experienced during DJF and OND seasons over southern and equatorial GHA regions respectively. Notably, as precipitation increases, the deficit (E < P) between evaporation (E) and precipitation (P) increased over the years, with a negatively skewed distribution. This generally implies that there is a high likelihood of an increased deficit in local moisture supply. This remarkable change in the general hydrological cycle (i.e. deficit in local moisture) is projected to be also coincident with intensified westerly anomaly influx from the Congo basin into the region. However, better understanding of the detailed changes in hydrological cycle will require comprehensive water budget analyses that require daily or sub-daily variables, and was not a specific focus of the present study. [ABSTRACT FROM AUTHOR]

Previous studies indicated that boreal spring North Atlantic horseshoe-like (NAH) sea surface temperature (SST) anomaly pattern has a significant impact on the surface air temperature (SAT) variation over the mid-high latitudes of Eurasia via an atmospheric wave train. This study investigates the connection of springtime NAH SST anomaly and the Eurasian SAT variation in thirty-two coupled climate models that participated in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Most of the CMIP5 models simulate well the spatial pattern of SST anomalies in the North Atlantic related to the spring NAH SST, but overestimate the spatial standard deviation. There exists large spreads in the connection of the spring NAH SST anomaly pattern and the Eurasian SAT variation among the CMIP5 models. The models that capture the observed spring NAH SST-Eurasian SAT connection can well reproduce the observed atmospheric wave train, in particular, with a marked anticyclonic anomaly over north Europe and a cyclonic anomaly over central Eurasia. This wave train results in above-normal SAT over west Europe and eastern Eurasia and below-normal SAT over central Eurasia. By contrast, the models that fail to reproduce the spring NAH SST-related Eurasian SAT anomalies show limitations in simulating the atmospheric wave train. Further analysis suggests that the models with larger climatological westerly winds over the mid-latitude North Atlantic and high-latitude Eurasia as well as lower climatological SST in the tropical northern Atlantic have a better ability in simulating the observed atmospheric wave train over the North Atlantic and Eurasia and thus perform better in simulating the spring NAH SST-Eurasian SAT connection. [ABSTRACT FROM AUTHOR]

Previous studies have revealed some common biases in coupled general circulation model's simulations of the East Asian (EA) winter monsoon (EAWM), including colder surface air temperature and more winter precipitation over the EA region. In this study, we examined 41 fully coupled atmosphere-ocean models from fifth phase of the Coupled Model Intercomparison Project (CMIP5), which will be widely used in the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC), and address whether the current state-of-the-art CMIP5 models can characterise the climatology of the East Asian winter monsoon. We also compared the results with the models from third phase of CMIP, which was extensively used in the fourth assessment report of the IPCC. The results show that the cold surface air temperature (SAT) bias is lessened and the precipitation amount decreased with the current CMIP5 models. Moreover, the CMIP5 models performbetter at predicting surface winds and high-level jet streams than the CMIP3 models. Moreover, CMIP5 models show more model consistency in most EAWM parameters, and the interannual variability of the SAT is closer to the observations. We also examined the change in the radiation energy budget in the CMIP5 models and compared with CMIP3 models. Although the improvements are significant, deficiencies still exist in the simulation of the EAWM, e.g., the stronger EA major trough and the stronger zonal sea level pressure gradient. [ABSTRACT FROM AUTHOR]