The stratospheric climate and variability from simulations of sixteen chemistry‐climate models is evaluated. On average the polar night jet is well reproduced though its variability is less well reproduced with a large spread between models. Polar temperature biases are less than 5 K except in the Southern Hemisphere (SH) lower stratosphere in spring. The accumulated area of low temperatures responsible for polar stratospheric cloud formation is accurately reproduced for the Antarctic but underestimated for the Arctic. The shape and position of the polar vortex is well simulated, as is the tropical upwelling in the lower stratosphere. There is a wide model spread in the frequency of major sudden stratospheric warnings (SSWs), late biases in the breakup of the SH vortex, and a weak annual cycle in the zonal wind in the tropical upper stratosphere. Quantitatively, “metrics” indicate a wide spread in model performance for most diagnostics with systematic biases in many, and poorer performance in the SH than in the Northern Hemisphere (NH). Correlations were found in the SH between errors in the final warming, polar temperatures, the leading mode of variability, and jet strength, and in the NH between errors in polar temperatures, frequency of major SSWs, and jet strength. Models with a stronger QBO have stronger tropical upwelling and a colder NH vortex. Both the qualitative and quantitative analysis indicate a number of common and long‐standing model problems, particularly related to the simulation of the SH and stratospheric variability

"Climate change is expected to have serious environmental, economic, and social impacts on South Africa. In particular, rural farmers, whose livelihoods depend on the use of natural resources, are likely to bear the brunt of adverse impacts. The extent to which these impacts are felt depends in large part on the extent of adaptation in response to climate change. This research uses a “bottom-up” approach, which seeks to gain insights from the farmers themselves based on a farm household survey. Farm-level data were collected from 794 households in the Limpopo River Basin of South Africa for the farming season 2004–2005. The study examines how farmer perceptions correspond with climate data recorded at meteorological stations in the Limpopo River Basin and analyzes farmers' adaptation responses to climate change and variability. A Heckman probit model and a multinomial logit (MNL) model are used to examine the determinants of adaptation to climate change and variability. The statistical analysis of the climate data shows that temperature has increased over the years. Rainfall is characterized by large interannual variability, with the previous three years being very dry. Indeed, the analysis shows that farmers' perceptions of climate change are in line with the climatic data records. However, only approximately half of the farmers have adjusted their farming practices to account for the impacts of climate change. Lack of access to credit was cited by respondents as the main factor inhibiting adaptation. The results of the multinomial logit and Heckman probit models highlighted that household size, farming experience, wealth, access to credit, access to water, tenure rights, off-farm activities, and access to extension are the main factors that enhance adaptive capacity. Thus, the government should design policies aimed at improving these factors. " from authors' abstractClimate change, Climate variability, Perception, Adaptation, Agriculture,

This latest Climate Council report provides an update of climate change science, impacts and risks. This report draws from the massive body of evidence that human activities - primarily from the burning of coal, oil and gas - are driving dramatic changes in our climate system. The report outlines how the changing climate poses substantial and escalating risks for health, property, infrastructure, agriculture and natural ecosystems in Australia. Compared to our understanding when the last Critical Decade report was published, the risks of climate change for our well-being now look more serious at lower levels of climate change, strengthening the case for urgent action. Finally, the report describes why it is in Australia’s national interest to play a leadership role in the global move for strong climate action leading up to the Paris climate conference at the end of 2015

The connection between the El Ni˜no Southern Oscillation (ENSO) and the Northern polar stratosphere has been established from observations and atmospheric modeling. Here a systematic inter-comparison of the sensitivity of the modeled stratosphere to ENSO in Chemistry Climate Models (CCMs) is reported. This work uses results from a number of the CCMs included in the 2006 ozone assessment. In the lower stratosphere, the mean of all model simulations reports a warming of the polar vortex during strong ENSO events in February–March, consistent with but smaller than the estimate from satellite observations and ERA40 reanalysis. The anomalous warming is associated with an anomalous dynamical increase of column ozone north of 70� N that is accompanied by coherent column ozone decrease in the Tropics, in agreement with that deduced from the NIWA column ozone database, implying an increased residual circulation in the mean of all model simulations during ENSO. The spread in the model responses is partly due to the large internal stratospheric variability and it is shown that it crucially depends on the representation of the tropospheric ENSO teleconnection in the models

<div>Atmospheric spatial and temporal variability are closely related with the former being relatively well observed compared to the latter. The former is also regularly assessed in the validation of numerical weather prediction models while the latter is more difficult to estimate. Likewise, thermodynamical fields and circulation are closely coupled calling for an approach that considers them simultaneously.  </div> <div>In this contribution, spatio-temporal variability spectra of the four major reanalysis datasets are discussed and applied for the validation of a climate model prototype.  A relationship between deficiencies in simulated variability and model biases is derived. The underlying method includes dynamical regime decomposition thereby providing a better understanding of the role of tropical variability in global circulation. </div> <div>Results of numerical simulations are validated by a 20th century reanalysis. A climate model was forced either with the prescribed SST or with a slab ocean model that updates SST in each forecast step.  Scale-dependent validation shows that missing temporal variance in the model relative to verifying reanalysis increases as the spatial scale reduces that appears associated with an increasing lack of spatial variance at smaller scales. Similar to variability, bias is strongly scale dependent; the larger the scale, the greater the bias. Biases present in the SST-forced simulation increase in the simulation using the slab ocean. The comparison of biases computed as a systematic difference between the model and reanalysis and between the SST-forced model and slab-ocean model (a perfect-model scenario) suggests that improving the atmospheric model increases the variance in the model on synoptic and subsynoptic scales but large biases associated with a poor SST remain at planetary scales.</div> <p> </p>

A simulation with the climate–chemistry model (CCM) E39/C is presented, which covers both the troposphere and stratosphere dynamics and chemistry during the period 1960 to 1999. Although the CCM, by its nature, is not exactly representing observed day-by-day meteorology, there is an overall model's tendency to correctly reproduce the variability pattern due to an inclusion of realistic external forcings, like observed sea surface temperatures (e.g. El Niño), major volcanic eruption, solar cycle, concentrations of greenhouse gases, and Quasi-Biennial Oscillation. Additionally, climate–chemistry interactions are included, like the impact of ozone, methane, and other species on radiation and dynamics, and the impact of dynamics on emissions (lightning). However, a number of important feedbacks are not yet included (e.g. feedbacks related to biogenic emissions and emissions due to biomass burning). The results show a good representation of the evolution of the stratospheric ozone layer, including the ozone hole, which plays an important role for the simulation of natural variability of tropospheric ozone. Anthropogenic NOx emissions are included with a step-wise linear trend for each sector, but no interannual variability is included. The application of a number of diagnostics (e.g. marked ozone tracers) allows the separation of the impact of various processes/emissions on tropospheric ozone and shows that the simulated Northern Hemisphere tropospheric ozone budget is not only dominated by nitrogen oxide emissions and other ozone pre-cursors, but also by changes of the stratospheric ozone budget and its flux into the troposphere, which tends to reduce the simulated positive trend in tropospheric ozone due to emissions from industry and traffic during the late 80s and early 90s. For tropical regions the variability in ozone is dominated by variability in lightning (related to ENSO) and stratosphere– troposphere exchange (related to Northern Hemisphere Stratospheric dynamics and solar activity). Since tropospheric background chemistry is regarded only, the results are quantitatively limited with respect to derived trends. However, the main results are regarded to be robust. Although the horizontal resolution is rather coarse in comparison to regional models, such kind of simulations provide useful and necessary information on the impact of large-scale processes and inter-annual/decadal variations on regional air quality

Global climate models are the main tool of climate scientists who investigate the impact of human action on future climate change. Run on supercomputers, these models can simulate global interdependencies of diverse climate factors such as ocean circulation, sea level rise, or vegetation growth. They also integrate socio-economic models that predict population growth, GDP, actions of cities and transportation, and air pollution. However, different models or model ensembles produce different future scenarios because the systems that are modeled are extremely chaotic: they contain a lot of “noise” and the slightest change in assumed background conditions or the recognition of feedback processes between, for example, ocean surface temperatures and cloud formations, will produce very different future scenarios. Since the apparent differences between climate models are due to both random internal climate variability and real structural differences in model physics, climate scientists are running model experiments (computer simulations) that compare the performance of the different models in order to better understand the physics behind them. <br> Video available at: https://vimeo.com/527397952

Several studies have examined the link between climate and health, mainly focusing on the short term impacts of extreme temperatures. This paper analyzes instead the long term relation between climate variability and health using Swedish temperature and mortality data for the period 1751-2004 using different time scales. We find that periods with higher temperature are associated with lower mortality. The results indicate that long term climate variations in annual mean temperatures and not short term variations explain the connection between temperature and mortality. Considering annual extreme temperatures, we find that extreme low winter temperatures are correlated with higher short term mortality. We identify the impact of the 11-year solar cycle on crop yields as a possible explanation for our findings. The results have besides their economic-historical merits implications for modern day policy for developing countries, especially since the correlation with solar activity implies predictability.Mortality; Wavelet; Climate

The eastern equatorial Atlantic (EEA) seasonal cycle and interannual variability strongly influence the climate of the surrounding continents. It is thus crucial that models used in both climate predictions and future climate projections are able to simulate them accurately. In that context, the EEA seasonal cycle and interannual variability are evaluated over the period 1985–2004 in models participating to the Ocean Model Intercomparison Project Phases 1 and 2 (OMIP1 and OMIP2). The main difference between OMIP1 and OMIP2 simulations is their atmospheric forcing: CORE-II and JRA55-do, respectively. Seasonal cycles of the equatorial Atlantic zonal winds, sea level anomaly and sea surface temperature in OMIP1 and OMIP2 are comparable to reanalysis datasets. Yet, some discrepancies exist in both OMIP ensembles: the thermocline is too diffusive and there is a lack of cooling during the development of the Atlantic cold tongue. In addition, the vertical ocean velocity in the eastern equatorial Atlantic in boreal summer is larger in OMIP1 than in OMIP2 simulations. The EEA interannual sea surface temperature variability in the OMIP1 ensemble mean is found to be 51 % larger (0.62 ± 0.04 °C) than the OMIP2 ensemble mean (0.41 ± 0.03 °C). Sensitivity experiments demonstrate that the discrepancy in interannual sea surface temperature variability between OMIP1 and OMIP2 is mainly attributed to their wind forcing. While the April-May-June zonal wind variability in the western equatorial Atlantic is similar in both forcing, the zonal wind variability peaks in April for JRA55-do and in May for CORE-II. Differences in surface heat fluxes between the atmospheric forcing datasets have no significant impacts on the simulated interannual SST variability.

This bibliography is intended for people who are involved in fisheries, aquaculture, climate change, disaster management and policy development in West Africa or interested in one or more of these issues. The literature in this bibliography includes peer-reviewed journals, books and book chapters, grey reports and institutional technical papers, but is restricted to literature in English. They were gathered through an extensive web search using fisheries, fish, coastal, inland, aquaculture and/or in combination with climate change and impacts, climate variability, specific country names, West Africa and Gulf of Guinea as the main keywords.Fisheries, Climatic change, Aquaculture, Inland fisheries, Bibliographies, Disasters, Africa, West,

Global hydrological models (GHMs) are widely used in climate change impact assessments to estimate future changes in freshwater availability, droughts, and floods. Their suitability is often evaluated by comparing simulated daily or monthly streamflow with historical observations. However, since climate change impacts are usually expressed as changes relative to a reference period, biases in flow magnitude do not necessarily prevent GHMs from accurately representing hydrological change. Here, we propose an alternative method for evaluating the suitability of GHMs for quantifying the impact of climate change on water resources. This method is based on the assumption that GHMs that are more effective in capturing the timing and magnitude of the annual streamflow anomaly during a historical period (which is mainly caused by climate variability) are likely to produce more plausible hydrological responses to climate change. Therefore, it compares simulated and observed absolute and relative streamflow anomalies instead of streamflow magnitudes themselves; it considers annually aggregated streamflow, which, compared with daily or monthly streamflow, is much less impacted by reservoir operations and human water use — two important drivers of streamflow that are difficult to model using GHMs. In this study, we test this new evaluation method as an example for three GHMs (H08, MIROC-INTEG-LAND, and WaterGAP2.2e), forced by two climate datasets (20CRv3–ERA5 and 20CRv3–W5E5). We use streamflow observations from 589 gauging stations worldwide to evaluate the method. Magnitude-based evaluation shows large performance differences among the GHMs, with negative median Nash–Sutcliffe Efficiency (NSE) values for the two uncalibrated ones due to large biases. In contrast, all GHMs achieve positive NSE for annual streamflow anomalies. Regarding relative anomalies, the land surface model MIROC-INTEG-LAND performs worst with an NSE of about 0.3, whereas the two water resources models perform very similarly and achieve median NSE values above 0.5. Both models show very similar correlation, but WaterGAP simulates the standard deviation of the absolute and relative annual streamflow anomaly better than H08. The differences in performance between the two climate forcings are smaller than the differences among the models. In terms of the ability of GHMs to simulate the years in which extreme wet and dry anomalies occur, there is exact agreement between the observed and simulated extreme years at only 7–13 % of analysis locations. Meanwhile, the observed extreme year is identified among the five most extreme simulated years at 40–59 % of analysis locations. MIROC-INTEG-LAND also shows the lowest performance regarding extreme anomalies. GHM evaluation based on annual streamflow anomalies, particularly relative anomalies, is suitable for assessing how GHMs translate annual climate variability and, to a certain extent, climate change into hydrological changes. However, the proposed GHM evaluation method does not consider the vegetation response to increased atmospheric CO2 concentrations and climatic changes that may strongly affect the hydrological response to climate change. Therefore, multi-model ensemble assessments of hydrological impacts of climate change should include even lower-performing GHMs, provided that these GHMs take the vegetation response into account.

The representation of the Indian summer monsoon (ISM) circulation in some current chemistry–climate models (CCMs) is assessed. The main assessment focuses on the anticyclone that forms in the upper troposphere and lower stratosphere and the related changes in water vapor and ozone during July and August for the recent past. The synoptic structures are described and CCMs and reanalysis models are compared. Multiannual means and weak versus strong monsoon cases as classified by the Monsoon–Hadley index (MHI) are discussed. The authors find that current CCMs capture the average synoptic structure of the ISM anticyclone well as compared to the 40-yr ECMWF Re-Analysis (ERA-40) and NCEP–NCAR reanalyses. The associated impact on water vapor and ozone in the upper troposphere and lower stratosphere as observed with the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat is captured by most models to some degree. The similarities for the strong versus weak monsoon cases are limited, and even for present-day conditions the models do not agree well for extreme events. Nevertheless, some features are present in the reanalyses and more than one CCM, for example, ozone increases at 380 K eastward of the ISM. With the database available for this study, future changes of the ISM are hard to assess. The modeled monsoon activity index used here shows slight weakening of the ISM circulation in a future climate, and some of the modeled water vapor increase seems to be contained in the anticyclone at 360 K and sometimes above. The authors conclude that current CCMs capture the average large-scale synoptic structure of the ISM well during July and August, but large differences for the interannual variability make assessments of likely future changes of the ISM highly uncertain

&lt;p&gt;The climate's mean state reflects only part of the changing climate and how it affects everyday lives. Understanding the climate's variability is crucial to provide more reliable simulations and projections, but temporal and spatial variability patterns and how they are related to changes in the mean state remain unclear. Here, we examine changes in variability since the Last Glacial in response to the warming of the global climate by several degrees. The analysis uses simulations from climate models of different complexity: a two-dimensional energy balance model (TransEBM), an earth system model of intermediate complexity (LoveClim), and a general circulation model (HadCM3). We analyse the simulated variability with respect to the different processes and parameterizations included in the different models and compare the temporal and spatial patterns that emerge. Commonalities as well as differences between models and how they relate to the changing mean state show that fast, low complexity models can capture a range of features of a climate variable's development, but also where such reduced descriptions fall short. As such, the results offer implications for the complexity that is needed and sufficient in parameterizations of climatic processes. Furthermore, we envisage that a comparison to paleoclimate archives can provide limits on the temporal and spatial scales that dominate the variability of climate.&lt;/p&gt;

The influence of climate on economic growth is a topic of growing interest. Few studies have investigated the potential role that climate hazards and their cumulative effects have on the growth prospects for a country. Due to the relatively stationary spatial patterns of global climate, some regions and countries are more prone to climate hazards and climate variability than others. This study uses a precipitation index that preserves the spatial and temporal variability of precipitation and differentiates between precipitation maximums (such as floods) and minimums (such as droughts). The authors develop a year and country fixed effects regression model to test the influence of climate variables on measures of economic growth and activity. The results indicate that precipitation extremes (floods and droughts) are the dominant climate influence on economic growth and that the effects are significant and negative. The drought index is associated with a highly significant negative influence on growth of growth domestic product, while the flood index is associated with a negative influence on growth of gross domestic product and lagged effects on growth. Temperature has little significant effect. These results have important implications for economic projections of climate change impacts. In addition, adaptation strategies should give new consideration to the importance of water resources given the identification of precipitation extremes as the key climate influence on historical growth of gross domestic product.Climate Change Mitigation and Green House Gases,Science of Climate Change,Global Environment Facility,Climate Change Economics,Climate Change Impacts

Models often underestimate blocking in the Atlantic and Pacific basins and this can lead to errors in both weather and climate predictions. Horizontal resolution is often cited as the main culprit for blocking errors due to poorly resolved small-scale variability, the upscale effects of which help to maintain blocks. Although these processes are important for blocking, the authors show that much of the blocking error diagnosed using common methods of analysis and current climate models is directly attributable to the climatological bias of the model. This explains a large proportion of diagnosed blocking error in models used in the recent Intergovernmental Panel for Climate Change report. Furthermore, greatly improved statistics are obtained by diagnosing blocking using climate model data corrected to account for mean model biases. To the extent that mean biases may be corrected in low-resolution models, this suggests that such models may be able to generate greatly improved levels of atmospheric blocking