Respiration of Recently-Fixed Plant Carbon Dominates Mid-Winter Ecosystem CO2 Production in Sub-Arctic Heath Tundra

Arctic ecosystems could provide a substantial positive feedback to global climate change if warming stimulates below-ground CO₂ release by enhancing decomposition of bulk soil organic matter reserves.Ecosystem respiration during winter is important in this context because CO₂ release from snow-covered tundra soils is a substantial component of annual net carbon (C) balance, and because global climate models predict that the most rapid rises in regional air temperature will occur in the Arctic during winter. In this manipulative field study, the relative contributions of plant and bulk soil organic matter C pools to ecosystem CO₂ production in mid-winter were investigated. We measured CO₂ efflux rates in Swedish sub-arctic heath tundra from control plots and from plots that had been clipped in the previous growing season to disrupt plant activity. Respiration derived from recently-fixed plant C (i.e., plant respiration, and respiration associated with rhizosphere exudates and decomposition of fresh litter) was the principal source of CO₂ efflux, while respiration associated with decomposition of bulk soil organic matter was low, and appeared relatively insensitive to temperature. These results suggest that warmer mid-winter temperatures in the Arctic may have a much greater impact on the cycling of recently-fixed, plant-associated C pools than on the depletion of tundra bulk soil C reserves, and consequently that there is a low potential for significant initial feedbacks from arctic ecosystems to climate change during mid-winter.     

Historical and Modern Fluctuations of Lakes Tanganyika and Rukwa and Their Relationship to Rainfall Variability

This paper describes the fluctuations of Lakes Tanganyika and Rukwa over the last two centuries. Lake chronologies extending back to the late eighteenth century are derived from reports of European visitors, settlers and explorers and from oral accounts of the local peoples. The historical fluctuations are meshed with the modern record to provide a picture of the lakes' fluctuations until the late twentieth century. The historical fluctuations of the lakes are quite similar. The most important of these are low levels during the first half of the nineteenth century, very high stands in the last decades of the nineteenth century, and, around the turn of the century, a rapid fall to twentieth century levels. This pattern is ubiquitous throughout eastern Africa and is apparent in numerous other lakes, including Victoria, Naivasha, Stefanie, Turkana, Malawi and Chilwa. Driest periods were in the 1920s or earlier and in the 1950s. Lake Tanganyika returned to extremely high stands in the 1960s and has continued to maintain relatively high stands since that time. Lake Rukwa rose to high stands during the 1980s and maintained them for several years. An analysis of rainfall variability shows that these trends are generally explained by variations in catchment rainfall. However, the lakes' responses to rainfall variability are sometimes dissimilar because Lake Rukwa is a closed basin. Our results demonstrate the complexity of the rainfall/lake-level relationships and the need to use water balance relationships in order to interpret the lakes' historical or paleo-fluctuations in terms of rainfall.     

A global perspective on African climate

We describe the global climate system context in which to interpret African environmental change to support planning and implementation of policymaking action at national, regional and continental scales, and to inform the debate between proponents of mitigation v. adaptation strategies in the face of climate change. We review recent advances and current challenges in African climate research and exploit our physical understanding of variability and trends to shape our outlook on future climate change. We classify the various mechanisms that have been proposed as relevant for understanding variations in African rainfall, emphasizing a “tropospheric stabilization” mechanism that is of importance on interannual time scales as well as for the future response to warming oceans. Two patterns stand out in our analysis of twentieth century rainfall variability: a drying of the monsoon regions, related to warming of the tropical oceans, and variability related to the El Niño–Southern Oscillation. The latest generation of climate models partly captures this recent continent-wide drying trend, attributing it to the combination of anthropogenic emissions of aerosols and greenhouse gases, the relative contribution of which is difficult to quantify with the existing model archive. The same climate models fail to reach a robust agreement regarding the twenty-first century outlook for African rainfall, in a future with increasing greenhouse gases and decreasing aerosol loadings. Such uncertainty underscores current limitations in our understanding of the global climate system that it is necessary to overcome if science is to support Africa in meeting its development goals.     

A microscale three-dimensional urban energy balance model for studying surface temperatures

A microscale three-dimensional (3-D) urban energy balance model, Temperatures of Urban Facets in 3-D (TUF-3D), is developed to predict urban surface temperatures for a variety of surface geometries and properties, weather conditions, and solar angles. The surface is composed of plane-parallel facets: roofs, walls, and streets, which are further sub-divided into identical square patches, resulting in a 3-D raster-type model geometry. The model code is structured into radiation, conduction and convection sub-models. The radiation sub-model uses the radiosity approach and accounts for multiple reflections and shading of direct solar radiation. Conduction is solved by finite differencing of the heat conduction equation, and convection is modelled by empirically relating patch heat transfer coefficients to the momentum forcing and the building morphology. The radiation and conduction sub-models are tested individually against measurements, and the complete model is tested against full-scale urban surface temperature and energy balance observations. Modelled surface temperatures perform well at both the facet-average and the sub-facet scales given the precision of the observations and the uncertainties in the model inputs. The model has several potential applications, such as the calculation of radiative loads, and the investigation of effective thermal anisotropy (when combined with a sensor-view model).     

Operational climate prediction in the era of big data in China: Reviews and prospects

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.     

Influence of the Boreal Summer Intraseasonal Oscillation on Extreme Temperature Events in the Northern Hemisphere

The impact of the boreal summer intraseasonal oscillation (BSISO) on extreme hot and cool events was investigated, by analyzing the observed and reanalysis data for the period from 1983 to 2012. It is found that the frequency of the extreme events in middle and high latitudes is significantly modulated by the BSISO convection in the tropics, with a 3–9-day lag. During phases 1 and 2 when the BSISO positive rainfall anomaly is primarily located over a northwest–southeast oriented belt extending from India to Maritime Continent and a negative rainfall anomaly appears in western North Pacific, the frequency of extreme hot events is 40% more than the frequency of non-extreme hot events. Most noticeable increase appears in midlatitude North Pacific (north of 40°N) and higher-latitude polar region.Two physical mechanisms are primarily responsible for the change of the extreme frequency. First, an upper-tropospheric Rossby wave train (due to the wave energy propagation) is generated in response to a negative heating anomaly over tropical western North Pacific in phases 1 and 2. This wave train consists of a strong high pressure anomaly center northeast of Japan, a weak low pressure anomaly center over Alaska, and a strong high pressure anomaly center over the western coast of United States. Easterly anomalies to the south of the two strong midlatitude high pressure centers weaken the climatological subtropical jet along 40°N, which is accompanied by anomalous subsidence and warming in North Pacific north of 40°N. Second, an enhanced monsoonal heating over South Asia and East Asia sets up a transverse monsoonal overturning circulation, with large-scale ascending (descending) anomalies over tropical Indian (Pacific) Ocean. Both the processes favor more frequent extreme hot events in higher-latitude Northern Hemisphere. An anomalous atmospheric general circulation model is used to confirm the tropical heating effect.     

Assessment of Gradient-Based Similarity Functions in the Stable Boundary Layer Derived from a Large-Eddy Simulation

Most of our knowledge on forest-edge flows comes from numerical and wind-tunnel experiments where canopies are horizontally homogeneous. To investigate the impact of tree-scale heterogeneities (\({>}1\) m) on the edge-flow dynamics, the flow in an inhomogeneous forest edge on Falster island in Denmark is investigated using large-eddy simulation. The three-dimensional forest structure is prescribed in the model using high resolution helicopter-based lidar scans. After evaluating the simulation against wind measurements upwind and downwind of the forest leading edge, the flow dynamics are compared between the scanned forest and an equivalent homogeneous forest. The simulations reveal that forest inhomogeneities facilitate flow penetration into the canopy from the edge, inducing important dispersive fluxes in the edge region as a consequence of the flow spatial variability. Further downstream from the edge, the forest inhomogeneities accentuate the canopy-top turbulence and the skewness of the wind-velocity components while the momentum flux remains unchanged. This leads to a lower efficiency in the turbulent transport of momentum within the canopy. Dispersive fluxes are only significant in the upper canopy. Above the canopy, the mean flow is less affected by the forest inhomogeneities. The inhomogeneities induce an increase in the mean wind speed that was found to be equivalent to a decrease in the aerodynamic height of the canopy. Overall, these results highlight the importance of forest inhomogeneities when looking at canopy–atmosphere exchanges in forest-edge regions.     

Validation of climate model output using Bayesian statistical methods

The growing interest in and emphasis on high spatial resolution estimates of future climate has demonstrated the need to apply regional climate models (RCMs) to that problem. As a consequence, the need for validation of these models, an assessment of how well an RCM reproduces a known climate, has also grown. Validation is often performed by comparing RCM output to gridded climate datasets and/or station data. The primary disadvantage of using gridded climate datasets is that the spatial resolution is almost always different and generally coarser than climate model output. We have used a Bayesian statistical model derived from observational data to validate RCM output. We used surface air temperature (SAT) data from 109 observational stations in California, all with records of approximately 50 years in length, and created a statistical model based on this data. The statistical model takes into account the elevation of the station, distance from coastline, and the NOAA climate region in which the station resides. Analysis indicates that the statistical model provides reliable estimates of the mean monthly SAT at any given station. In our method, the uncertainty in the estimates produced by the statistical model are directly determined by obtaining probability density functions for predicted SATs. This statistical model is then used to estimate average SATs corresponding to each of the climate model grid cells. These estimates are compared to the output of the RCM to assess how well the RCM matches the observed climate as defined by the statistical model. Overall, the match between the RCM output and the statistical model is good, with some deficiencies likely due in part to the representation of topography in the RCM.