Downscaling reveals diverse effects of anthropogenic climate warming on the potential for local environments to support malaria transmission

The potential impact of climate warming on patterns of malaria transmission has been the subject of keen scientific and policy debate. Standard climate models (GCMs) characterize climate change at relatively coarse spatial and temporal scales. However, malaria parasites and the mosquito vectors respond to diurnal variations in conditions at very local scales. Here we bridge this gap by downscaling a series of GCMs to provide high-resolution temperature data for four different sites and show that although outputs from both the GCM and the downscaled models predict diverse but qualitatively similar effects of warming on the potential for adult mosquitoes to transmit malaria, the predicted magnitude of change differs markedly between the different model approaches. Raw GCM model outputs underestimate the effects of climate warming at both hot (3-fold) and cold (8–12 fold) extremes, and overestimate (3-fold) the change under intermediate conditions. Thus, downscaling could add important insights to the standard application of coarse-scale GCMs for biophysical processes driven strongly by local microclimatic conditions.     

The improvement of greenhouse gas inventory as a tool for reduction emission uncertainties for operations with oil in the Russian Federation

The high quality inventory is an important step to greenhouse gas emission mitigation. The inventory quality is estimated by means of the uncertainty analysis. The level of uncertainty depends upon the reliability of activity data and the parameters used. An attempt has been made to improve the accuracy of the estimates through a shift from production-based method (IPCC Tier 1) (IPCC 2000) to enhanced combination of production-based and mass balance methods (IPCC Tier 2) (IPCC 2006) in the estimation of emissions from operations with oil that are key in the national greenhouse gas inventory of the Russian Federation. The IPCC Tier 2 (IPCC 2006) was adapted for the national conditions. The greenhouse gas emissions were calculated for 1990 to 2009 with the use of both methods. The quantitative uncertainty assessment of the calculations was performed, and the outcomes were compared. The comparison showed that the estimates made with the use of higher tier method resulted in higher accuracy and lower uncertainties (26 % respectively compared to previously derived 54 %).     

The response of the South Asian Summer Monsoon circulation to intensified irrigation in global climate model simulations

Agricultural intensification in South Asia has resulted in the expansion and intensification of surface irrigation over the twentieth century. The resulting changes to the surface energy balance could affect the temperature contrasts between the South Asian land surface and the equatorial Indian Ocean, potentially altering the South Asian Summer Monsoon (SASM) circulation. Prior studies have noted apparent declines in the monsoon intensity over the twentieth century and have focused on how altered surface energy balances impact the SASM rainfall distribution. Here, we use the coupled Goddard Institute for Space Studies ModelE-R general circulation model to investigate the impact of intensifying irrigation on the large-scale SASM circulation over the twentieth century, including how the effect of irrigation compares to the impact of increasing greenhouse gas (GHG) forcing. We force our simulations with time-varying, historical estimates of irrigation, both alone and with twentieth century GHGs and other forcings. In the irrigation only experiment, irrigation rates correlate strongly with lower and upper level temperature contrasts between the Indian sub-continent and the Indian Ocean (Pearson’s r = ?0.66 and r = ?0.46, respectively), important quantities that control the strength of the SASM circulation. When GHG forcing is included, these correlations strengthen: r = ?0.72 and r = ?0.47 for lower and upper level temperature contrasts, respectively. Under irrigated conditions, the mean SASM intensity in the model decreases only slightly and insignificantly. However, in the simulation with irrigation and GHG forcing, inter-annual variability of the SASM circulation decreases by ~40 %, consistent with trends in the reanalysis products. This suggests that the inclusion of irrigation may be necessary to accurately simulate the historical trends and variability of the SASM system over the last 50 years. These findings suggest that intensifying irrigation, in concert with increased GHG forcing, is capable of reducing the variability of the simulated SASM circulation and altering the regional moisture transport by limiting the surface warming and reducing land–sea temperature gradients.     

Development of satellite green vegetation fraction time series for use in mesoscale modeling: application to the European heat wave 2006

A method is presented for development of satellite green vegetation fraction (GVF) time series for use in the Weather Research and Forecasting (WRF) model. The GVF data is in the WRF model used to describe the temporal evolution of many land surface parameters, in addition to the evolution of vegetation. Several high-resolution GVF products, derived from high-quality satellite retrievals from Moderate Resolution Imaging Spectroradiometer images, were produced and their performance was evaluated in long-term WRF simulations. The atmospheric conditions during the 2006 heat wave year over Europe were simulated since significant interannual variability in vegetation seasonality was found. Such interannual variability is expected to increase in the coming decades due to climatic changes. The simulation using a quadratic normalized difference vegetation index to GVF relationship resulted in consistent improvements of modeled temperatures. The model mean temperature cold bias was reduced by 10 % for the whole domain and by 20–45 % in areas affected by the heat wave. The study shows that WRF simulations during heat waves and droughts, when vegetation conditions deviate from the climatology, require concurrent land surface properties in order to produce accurate results.     

Validation of BALTIMOS precipitation structures using BALTRAD radar data

The comparison of rain areas observed by the radar network BALTRAD and those produced by the model BALTIMOS shows that the model is able to reproduce the rain areas’ basic properties. These are power law distributed frequency distributions as well as lognormal rain area size distributions. The parameters governing these distributions are also matched by power laws evident for the observations and the BALTIMOS data. The distributions also provide evidence that the model produces too many large structures and too little small ones. However, the shape of these structures, represented by their fractal dimension, is well met. The perimeter of the model structures is only slightly smoother than that of the observed ones. The model is also able to reproduce the diurnal cycle of convective activity with emerging and decaying convective cells, though some deficiencies in the timing and the magnitude of the maximum occurrence of rain areas and peaks are evident. Additionally, it was found that the methods developed within this project are a valuable tool to validate BALTIMOS and potentially also other regional climate models.     

Indian Ocean dipole interpreted in terms of recharge oscillator theory

In this paper we use sea surface height (SSH) derived from satellite altimetry and an analytical linear equatorial wave model to interpret the evolution of the Indian Ocean Dipole (IOD) in the framework of recharge oscillator theory. The specific question we address is whether heat content in the equatorial band, for which SSH is a proxy, is a predictor of IOD development as it is for El Niño and the Southern Oscillation (ENSO) in the Pacific. We find that, as in the Pacific, there are zonally coherent changes in heat content along the equator prior to the onset of IOD events. These changes in heat content are modulated by wind-forced westward propagating Rossby waves in the latitude band 5°–10°S, which at the western boundary reflect into Kelvin waves trapped to the equator. The biennial character of the IOD is affected by this cycling of wave energy between 5° and 10°S and the equator. Heat content changes are a weaker leading indicator of IOD sea surface temperature anomaly development than is the case for ENSO in the Pacific though because other factors are at work in generating IOD variability, one of which is ENSO forcing itself through changes in the Walker Circulation.     

The day-to-day monitoring of the 2011 severe drought in China

Dry/wet condition has a large interannual variability. Decision-makers need to know the onset, duration, and intensity of drought, and require droughts be monitored at a daily to weekly scale. However, previous tools cannot monitor drought well at this short timescale. The Palmer Drought Severity Index has been found dissatisfactory in monitoring because of its complexity and numerous limitations. The Standardized Precipitation Index (SPI) always asks for a timescale, and precipitation is averaged over the period of the scale. Because of this, the SPI cannot be used for short scales, e.g., several days, and what it tells is the overall drought situation of the period. The weighted average of precipitation (WAP) developed by Lu (Geophys Res Lett 36:L12707, 2009) overcomes the deficiency of the SPI; it does not require a timescale, and can provide the drought (and flood) extent of each day. Therefore, the WAP can monitor drought at scales from daily to weekly, monthly, and any longer scale, and is really “flexible and versatile for all timescales”. In this study, the standardized WAP (SWAP) is used to monitor the 2011 drought over China. Drought swept the country during the year from north to south and from east to west. In spring, a once-in-a-fifty-year drought occurred over the Yangtze River basin and the southern region, causing serious shortage of drinking water for people and livestock, as well as tremendous losses in agriculture and the shipping industry. Results show that the SWAP, with its monthly mean plots, can well reproduce the seasonal shift of the 2011 drought across the country. The animation of daily plots demonstrates that the SWAP would have been able to monitor the day-to-day variation of the spring drought around the Yangtze River basin. It can provide the details of the drought, such as when the drought emerged over the region, how long it maintained there (though drought area may move back and forth with extension and contraction of the area), and when the drought relieved over the basin.     

Diapycnal heat flux and mixed layer heat budget within the Atlantic Cold Tongue

Sea surface temperatures (SSTs) in the eastern tropical Atlantic are crucial for climate variability within the tropical belt. Despite this importance, state-of-the-art climate models show a large SST warm bias in this region. Knowledge about the seasonal mixed layer (ML) heat budget is a prerequisite for understanding SST mean state and its variability. Within this study all contributions to the seasonal ML heat budget are estimated at four locations within the Atlantic cold tongue (ACT) that are representative for the western (0°N, 23°W), central (0°N, 10°W) and eastern (0°N, 0°E) equatorial as well as the southern (10°S, 10°W) ACT. To estimate the contribution of the diapycnal heat flux due to turbulence an extensive data set of microstructure observations collected during ten research cruises between 2005 and 2012 is analyzed. The results for the equatorial ACT indicate that with the inclusion of the diapycnal heat flux the seasonal ML heat budget is balanced. Within the equatorial region, the diapycnal heat flux is essential for the development of the ACT. It dominates over all other cooling terms in the central and eastern equatorial ACT, while it is of similar size as the zonal advection in the western equatorial ACT. In contrast, the SST evolution in the southern ACT region can be explained entirely by air-sea heat fluxes.     

Changes to the drivers of fire weather with a warming climate – a case study of southeast Tasmania

Projected changes to the global climate system have great implications for the incidence of large infrequent fires in many regions. Here we examine the synoptic-scale and local-scale influences on the incidence of extreme fire weather days and consider projections of the large-scale mean climate to explore future fire weather projections. We focus on a case study region with periodic extreme fire dangers; southeast Tasmania, Australia. We compare the performance of a dynamically downscaled regional climate model with Global Climate Model outputs as a tool for examining the local-scale influences while accounting for high regional variability. Many of the worst fires in Tasmania and the southeast Australian region are associated with deep cold fronts and strong prefrontal winds. The downscaled simulations reproduce this synoptic type with greater fidelity than a typical global climate model. The incidence of systems in this category is projected to increase through the century under a high emission scenario, driven mainly by an increase in the temperature of air masses, with little change in the strength of the systems. The regional climate model projected increase in frequency is smaller than for the global climate models used as input, with a large model range and natural variability. We also demonstrate how a blocking Foehn effect and topographic channelling contributed to the extreme conditions during an extreme fire weather day in Tasmania in January 2013. Effects such as these are likely to contribute to high fire danger throughout the century. Regional climate models are useful tools that enable various meteorological drivers of fire danger to be considered in projections of future fire danger.     

Scientific uncertainty and climate change: Part II. Uncertainty and mitigation

In public debate surrounding climate change, scientific uncertainty is often cited in connection with arguments against mitigative action. This article examines the role of uncertainty about future climate change in determining the likely success or failure of mitigative action. We show by Monte Carlo simulation that greater uncertainty translates into a greater likelihood that mitigation efforts will fail to limit global warming to a target (e.g., 2 °C). The effect of uncertainty can be reduced by limiting greenhouse gas emissions. Taken together with the fact that greater uncertainty also increases the potential damages arising from unabated emissions (Lewandowsky et al. 2014), any appeal to uncertainty implies a stronger, rather than weaker, need to cut greenhouse gas emissions than in the absence of uncertainty.