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Global climate change is expected to affect the ocean's biological productivity. The most comprehensive information available about the global distribution of contemporary ocean primary productivity is derived from satellite data. Large spatial patchiness and interannual to multidecadal variability in chlorophyll a concentration challenges efforts to distinguish a global, secular trend given satellite records which are limited in duration and continuity. The longest ocean color satellite record comes from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), which failed in December 2010. The Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color sensors are beyond their originally planned operational lifetime. Successful retrieval of a quality signal from the current Visible Infrared Imager Radiometer Suite (VIIRS) instrument, or successful launch of the Ocean Land Colour Instrument (OLCI) in 2013 will hopefully extend the ocean color time series and increase the potential for detecting trends in ocean productivity in the future. Alternatively, a potential discontinuity in the time series of ocean chlorophyll a, introduced by a change of instrument without overlap and opportunity for cross-calibration, would make trend detection even more challenging. In this paper, we demonstrate that there are a few regions with statistically significant trends over the ten years of SeaWiFS data, but at a global scale the trend is not large enough to be distinguished from noise. We quantify the degree to which red noise (autocorrelation) especially challenges trend detection in these observational time series. We further demonstrate how discontinuities in the time series at various points would affect our ability to detect trends in ocean chlorophyll a. We highlight the importance of maintaining continuous, climate-quality satellite data records for climate-change detection and attribution studies. [ABSTRACT FROM AUTHOR]

We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover, composition and height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, and are compared to scores based on the temporal or spatial mean value of the observations and a "random" model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), and the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global vegetation models (DGVMs). SDBM reproduces observed CO2 seasonal cycles, but its simulation of independent measurements of net primary production (NPP) is too high. The two DGVMs show little difference for most benchmarks (including the inter-annual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change impacts and feedbacks. [ABSTRACT FROM AUTHOR]

For the detection of climate change, not only the magnitude of a trend signal is of significance. An essential issue is the time period required by the trend to be detectable in the first place. An illustrative measure for this is Time of Emergence (ToE), i.e., the point in time when a signal finally emerges from the background noise of natural variability. We investigate the ToE of trend signals in different biogeochemical and physical surface variables utilizing a multi-model ensemble comprising simulations of 17 ESMs. We find that signals in ocean biogeochemical variables emerge on much shorter timescales than the physical variable sea surface temperature (SST). The ToE patterns of pCO2 and pH are spatially very similar to DIC, yet the trends emerge much faster -- after roughly 12 yr for the majority of the global ocean area, compared to between 10-30 yr for DIC and 45-90 yr for SST. In general, the background noise is of higher importance in determining ToE than the strength of the trend signal. In areas with high natural variability, even strong trends both in the physical climate and carbon cycle system are masked by variability over decadal timescales. In contrast to the trend, natural variability is affected by the seasonal cycle. This has important implications for observations, since it implies that intra-annual variability could question the representativeness of irregularly seasonal sampled measurements for the entire year and, thus, the interpretation of observed trends. [ABSTRACT FROM AUTHOR]

The direction and magnitude of soil organic carbon (SOC) changes in response to climate change depend on the spatial and vertical distributions of SOC. We estimated spatially-resolved SOC stocks from surface to C horizon, distinguishing active-layer and permafrost-layer stocks, based on geospatial analysis of 472 soil profiles and spatially referenced environmental variables for Alaska. Total Alaska state-wide SOC stock was estimated to be 77 Pg, with 61% in the active-layer, 27% in permafrost, and 12% in non-permafrost soils. Prediction accuracy was highest for the active-layer as demonstrated by highest ratio of performance to deviation (1.5). Large spatial variability was predicted, with whole-profile, active-layer, and permafrost-layer stocks ranging from 1-296 kg C m-2, 2-166 kg m-2, and 0-232 kg m-2, respectively. Temperature and soil wetness were found to be primary controllers of whole-profile, active-layer, and permafrost-layer SOC stocks. Secondary controllers, in order of importance, were: land cover type, topographic attributes, and bedrock geology. The observed importance of soil wetness rather than precipitation on SOC stocks implies that the poor representation of high-latitude soil wetness in Earth System Models may lead to large uncertainty in predicted SOC stocks under future climate change scenarios. Under strict caveats described in the text and assuming temperature changes from the A1B Intergovernmental Panel on Climate Change emissions scenario, our geospatial model indicates that the equilibrium average 2100 Alaska active-layer depth could deepen by 11 cm, resulting in a thawing of 13 Pg C currently in permafrost. The equilibrium SOC loss associated with this warming would be highest under continuous permafrost (31 %), followed by discontinuous (28 %), isolated (24.3 %), and sporadic (23.6 %) permafrost areas. Our high resolution mapping of soil carbon stock reveals the potential vulnerability of high-latitude soil carbon and can be used as a basis for future studies of anthropogenic and climatic perturbations. [ABSTRACT FROM AUTHOR]

Quantifying the interannual variability (IAV) of the terrestrial productivity and its sensitivity to climate is crucial for improving carbon budget predictions. However, the influence of climate and other mechanisms underlying the spatiotemporal patterns of IAV of productivity are not well understood. In this study we investigated the spatiotemporal patterns of IAV of historical observations of crop yields, tree ring width, remote sensing retrievals of FAPAR and NDVI, and other variables relevant to the terrestrial productivity in Europe in tandem with a set of climate variables. Our results reveal distinct spatial patterns in the IAV of most variables linked to terrestrial productivity. In particular, we find higher IAV in water-limited regions of Europe (Mediterranean and temperate continental Europe) compared to other regions. Our results further indicate that variations in the water balance during active growing season exert a more pronounced and direct effect than variations of temperature on explaining the spatial patterns in IAV of productivity related variables in temperate Europe. We also observe a temporally increasing trend in the IAV of terrestrial productivity and an increasing sensitivity of productivity to water availability in dry regions of Europe, which is likely attributable to the recently increased IAV of water availability in these regions. These findings suggest nonlinear responses of carbon fluxes to climate variability in Europe and that the IAV of terrestrial productivity has become more sensitive and more vulnerable to changes in water availability in the dry regions in Europe. The changing climate sensitivity of terrestrial productivity accompanied by the changing IAV of climate could impact carbon stocks and the net carbon balance of European ecosystems. [ABSTRACT FROM AUTHOR]

Regional climate change impact (CCI) studies have widely involved downscaling and bias-correcting (BC) Global Climate Model (GCM)-projected climate for driving land surface models. However, BC may cause uncertainties in projecting hydrologic and biogeochemical responses to future climate due to the impaired spatiotemporal covariance of climate variables and a breakdown of physical conservation principles. Here we quantify the impact of BC on simulated climate-driven changes in water variables (evapotranspiration, ET; runoff; snow water equivalent, SWE; and water demand for irrigation), crop yield, biogenic volatile organic compounds (BVOC), nitric oxide (NO) emissions, and dissolved inorganic nitrogen (DIN) export over the Pacific Northwest (PNW) Region. We also quantify the impacts on net primary production (NPP) over a small watershed in the region (HJ Andrews). Simulation results from the coupled ECHAM5/MPI-OM model with A1B emission scenario were firstly dynamically downscaled to 12 km resolutions with WRF model. Then a quantile mapping based statistical downscaling model was used to downscale them into 1/16th degree resolution daily climate data over historical and future periods. Two series climate data were generated according to the option of bias-correction (i.e. with bias-correction (BC) and without bias-correction, NBC). Impact models were then applied to estimate hydrologic and biogeochemical responses to both BC and NBC meteorological datasets. These impact models include a macro-scale hydrologic model (VIC), a coupled cropping system model (VIC-CropSyst), an ecohydrologic model (RHESSys), a biogenic emissions model (MEGAN), and a nutrient export model (Global-NEWS). Results demonstrate that the BC and NBC climate data provide consistent estimates of the climate-driven changes in water fluxes (ET, runoff, and water demand), VOCs (isoprene and monoterpenes) and NO emissions, mean crop yield, and river DIN export over the PNW domain. However, significant differences rise from projected SWE, crop yield from dry lands, and HJ Andrews's ET between BC and NBC data. Even though BC post-processing has no significant impacts on most of the studied variables when taking PNW as a whole, their effects have large spatial variations and some local areas are substantially influenced. In addition, there are months during which BC and NBC post-processing produces significant differences in projected changes, such as summer runoff. Factor-controlled simulations indicate that BC post-processing of precipitation and temperature both substantially contribute to these differences at region scales. We conclude that there are trade-offs between using BC climate data for offline CCI studies vs. direct modeled climate data. These trade-offs should be considered when designing integrated modeling frameworks for specific applications; e.g., BC may be more important when considering impacts on reservoir operations in mountainous watersheds than when investigating impacts on biogenic emissions and air quality (where VOCs are a primary indicator). [ABSTRACT FROM AUTHOR]

We develop a dynamic methanogen-available carbon model (DMCM) to quantify the role of the methanogen-available carbon pool in determining the spatial and temporal variability of tropical wetland CH4 emissions over seasonal timescales. We fit DMCM parameters to satellite observations of CH4 columns from SCIAMACHY CH4 and equivalent water height (EWH) from GRACE. Over the Amazon river basin we find substantial seasonal variability of this carbon pool (coefficient of variation=28±22 %) and a rapid decay constant (φ= 0.017 day-1), in agreement with available laboratory measurements, suggesting that plant litter is likely the prominent methanogen car bon source over this region. Using the DMCM we derive global CH4 emissions for 2003-2009, and determine the resulting seasonal variability of atmospheric CH4 on a global scale using the GEOS-Chem atmospheric chemistry and transport model. First, we estimate tropical emissions amount to 111.1 TgCH4 yr-1 of which 24% is emitted from Amazon wetlands. We estimate that annual tropical wetland emissions have in creased by 3.4 TgCH4 yr-1 between 2003 and 2009. Second, we find that the model is able to reproduce the observed seasonal lag between CH4 concentrations peaking 1-3 months before peak EWH values. We also find that our estimates of CH4 emissions substantially improve the comparison between the model and observed CH4 surface concentrations (r = 0.9). We anticipate that these new insights from the DMCM represent a fundamental step in parameterising tropical wetland CH4 emissions and quantifying the seasonal variability and future trends of tropical CH4 emissions. [ABSTRACT FROM AUTHOR]

Africa is becoming one of the most sensitive and vulnerable regions of the global ecosystem due to its variable climate, complex topography, and diversity of natural ecosystems. In the context of global warming, climate change not only alters the spatial distribution of temperature and precipitation in North Africa, but also affects the spatial distribution of vegetation as well as the structure and function of ecosystems, causing changes in the North African ecosystem and inducing a series of food security problems. In this regard, this paper analyzed the spatio-temporal distribution of climate change, climate production potential (CPP), and influencing factors in Africa based on meteorological data for 1901–2019, using the Thornthwaite Memorial model, Mann–Kendall mutation test, and Pearson correlation model. The results indicated that from 1901 to 2019, the CPP in North Africa decreased by 4.9%, while the region's precipitation experienced a decline of 5.2%. The phased trend in CPP was consistent with the change in precipitation. Temperature and precipitation were the main limiting factors for CPP in North Africa, with precipitation being more limiting. In general, North Africa's CPP was more sensitive to precipitation, and a continued 'warm and dry' climate in the future could lead to an increasing downward trend. [ABSTRACT FROM AUTHOR]

Streamflow in semiarid areas, especially in North China, was rapidly decreasing, which made it important to analyze the characteristics and influencing factors of streamflow. Using the hydro-meteorological data series of 1961–2017 in the upper Yongding River Basin (UYRB) (including the Yang River Basin (YRB) and Sanggan River Basin (SRB)), spatio-temporal variation characteristics of air temperature, precipitation, and potential evapotranspiration (E0) were analyzed. The results showed that precipitation has no significant trend; the temperature showed a significant increase of 0.1–0.5 °C per decade; E0 showed a significant decrease of approximately −2 mm/10yr (in 18 stations); the estimated rates of streamflow change were −7 and −8 mm/10yr for SRB and YRB. As for spatial distribution, the YRB presented a higher E0 value than the SRB; the mountain areas had more precipitation than the plain areas. The change points of streamflow occurred in 1982 and 2003. Both the Budyko and the DMC methods were used to evaluate the impacts of climate change and human activities on the mean annual streamflow. In variation stage I (1983~2003), impacts of human activities account for 90.6% and 62.7% of the mean annual streamflow changes in YRB and SRB, respectively. In variation stage II (2004~2017), the percentages were 99.5% and 93.5%, respectively. It is also noted that the first change point in streamflow was indeed at the beginning of China's land reform, when the farmers could manage their reallocated lands and, therefore, there was an increase in agricultural water consumption. The second change point coincided with "Capital Water Resources Planning", including water conservation projects and irrigation district construction programs. In general, human activities were mainly responsible for the significant decline in the annual streamflow of UYRB. This paper will provide valuable results for water resources planning and give guidance on the construction of water conservation function areas and ecological environment support areas in the capital. [ABSTRACT FROM AUTHOR]

In order to develop a system to estimate a country-scale soil organic carbon stock change (SCSC) in agricultural lands in Japan that enables to take account effect of land-use changes, climate, different agricultural activity and nature of soils, a spatially- explicit model simulation system using Rothamsted Carbon Model (RothC) integrated with spatial and temporal inventories was developed. Future scenarios on agricultural activity and land-use change were prepared, in addition to future climate projections by global climate models, with purposely selecting rather exaggerated and contrasting set of scenarios to assess system's sensitivity as well as to better factor out direct human influence in the SCSC accounting. Simulation was run from year 1970 to 2008, and to year 2020, with historical inventories and future scenarios involving target set in agricultural policy, respectively, and subsequently until year 2100 with no temporal changes in land-use and agricultural activity but with varying climate to investigate course of SCSC. Results of the country-scale SCSC simulation have indicated that conversion of paddy fields to croplands occurred during past decades, as well as a large conversion of agricultural fields to settlements or other lands that have occurred in historical period and would continue in future, could act as main factors causing greater loss of soil organic carbon (SOC) at country-scale, with reduction organic carbon input to soils and enhancement of SOC decomposition by transition of soil environment to aerobic conditions, respectively. Scenario analysis indicated that an option to increase organic carbon input to soils with intensified rotation with suppressing conversion of agricultural lands to other land-use types could achieve reduction of CO2 emission due to SCSC in the same level as that of another option to let agricultural fields be abandoned. These results emphasize that land-use changes, especially conversion of the agricultural lands to other land-use types by abandoning or urbanization accompanied by substantial changes in the rate of organic carbon input to soils, could cause a greater or comparable influence on country-scale SCSC compared with changes in management of agricultural lands. A net-net based accounting on SCSC showed potential influence of variations in future climate on SCSC, that highlighted importance of application of process-based model for estimation of this quantity. Whereas a baseline-based accounting on SCSC was shown to have robustness over variations in future climate and effectiveness to factor out direct human-induced influence on SCSC. Validation of the system's function to estimate SCSC in agricultural lands, by comparing simulation output with data from nation-wide stationary monitoring conducted during year 1979-1998, suggested that the system has an acceptable levels of validity, though only for limited range of conditions at current stage. In addition to uncertainties in estimation of the rate of organic carbon input to soils in different land-use types at large-scale, time course of SOC sequestration, supposition on land-use change pattern in future, as well as feasibility of agricultural policy planning are considered as important factors that need to be taken account in estimation on a potential of country-scale SCSC. [ABSTRACT FROM AUTHOR]

Catchments characteristics, such as geomorphology, geology, soil, land use, and climatic variables, play an important role in total stream flow responses, a critical resource for people and the environment. Most of the previous literatures were applied a conventional statistical regression model to assess the relationship between landscape-climate descriptors, and streamflow and PET. However, a conventional statistical regression model didn't consider dependence of explanatory variables that were collected or extracted across both space and time. This paper investigated the impacts of landscape attributes and climate variables on catchment scale temporal variation of total streamflow and spatio-temporal variation of potential evapotranspiration (PET) in the Mille catchment using multiple linear regression techniques, and the importance of this study was to test spatial autocorrelation in the spatial regression model which is required to properly assess and quantify the relationship between hydrological regime response components and Landscape-climate descriptors in a catchment with topographically complex, and high spatio-temporal climatic variation like in our case study area, the Mille catchment. Statistical regression analysis revealed significant relationships between streamflow and climate variables, especially with rainfall. Mean maximum temperature is the most dominant factor controlling temporal variation of potential evapotranspiration at a monthly scale, whereas NDVI is the most significant factor that controls the spatial variability of PET. The multiple regression model shows that 91.1% of temporal variation in streamflow was accounted for rainfall, whereas, 96.6% and 78.4% of temporal and spatial variation in potential evapotranspiration was accounted for in maximum temperature and NDVI, respectively. Methods also can be applied to catchments with similar landscape attributes and climate variables. [ABSTRACT FROM AUTHOR]

A range of applications analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate-sensitive infections (CSIs) and agriculture crop modelling, make use of land surface modelling (LSM) to predict future land surface conditions. There are multiple LSMs to choose from that account for land processes in different ways and this may introduce predictive uncertainty when LSM outputs are used as inputs to inform a given application. For useful predictions for a specific application, one must therefore understand the inherent uncertainties in the LSMs and the variations between them, as well as uncertainties arising from variation in the climate data driving the LSMs. This requires methods to analyse multivariate spatio-temporal variations and differences. A methodology is proposed based on multiway data analysis, which extends singular value decomposition (SVD) to multidimensional tables and provides spatio-temporal descriptions of agreements and disagreements between LSMs for both historical simulations and future predictions. The application underlying this paper is prediction of how climate change will affect the spread of CSIs in the Fennoscandian and north-west Russian regions, and the approach is explored by comparing net primary production (NPP) estimates over the period 1998–2013 from versions of leading LSMs (JULES, CLM5 and two versions of ORCHIDEE) that are adapted to high-latitude processes, as well as variations in JULES up to 2100 when driven by 34 global circulation models (GCMs). A single optimal spatio-temporal pattern, with slightly different weights for the four LSMs (up to 14 % maximum difference), provides a good approximation to all their estimates of NPP, capturing between 87 % and 93 % of the variability in the individual models, as well as around 90 % of the variability in the combined LSM dataset. The next best adjustment to this pattern, capturing an extra 4 % of the overall variability, is essentially a spatial correction applied to ORCHIDEE-HLveg that significantly improves the fit to this LSM, with only small improvements for the other LSMs. Subsequent correction terms gradually improve the overall and individual LSM fits but capture at most 1.7 % of the overall variability. Analysis of differences between LSMs provides information on the times and places where the LSMs differ and by how much, but in this case no single spatio-temporal pattern strongly dominates the variability. Hence interpretation of the analysis requires the summation of several such patterns. Nonetheless, the three best principal tensors capture around 76 % of the variability in the LSM differences and to a first approximation successively indicate the times and places where ORCHIDEE-HLveg, CLM5 and ORCHIDEE-MICT differ from the other LSMs. Differences between the climate forcing GCMs had a marginal effect up to 6 % on NPP predictions out to 2100 without specific spatio-temporal GCM interaction. [ABSTRACT FROM AUTHOR]

Extending across three major plateaus, namely the Qinghai-Tibetan Plateau, the Inner Mongolia-Xinjiang Plateau and the Loess Plateau, Northwest China has the complex terrain and spatio-temporal climate variations, and is affected by the interactions among different circulation systems, such as the summer monsoon, the westerlies and the plateau monsoon. The understanding of the climate variability, as well as its characteristics and evolution mechanisms in this area has been limited so far. In this paper, the precipitation characteristics and mechanisms in the eastern and western parts of Northwest China during the flood season are compared and analyzed based on the data from 192 national meteorological observational sites in Northwest China in 1961–2016. The results show that, divided by the northern boundary of the East Asian summer monsoon, there are huge differences in the precipitation variation characteristics between the eastern and western parts. The inter-annual variations, inter-decadal variations and total trends in the two parts all show a significant seesaw phenomenon. Moreover, it is found that the seesaw phenomenon of precipitation variation is closely related to the opposite variation between the East Asian summer monsoon index (MI) and the westerly circulation index (WI). In addition, the inverse variations on different time scales are only related to the contributions of precipitation at specific grades. Besides, in the two matching patterns of precipitation in the seesaw phenomenon, the middle and high latitudes are occupied by the "high-low-high" wave trains in the precipitation increases in the east of Northwest China (ENWC) and decreases in the west of Northwest China (WNWC) pattern, meaning precipitation increases in ENWC and decreases in WNWC. Whereas the opposite "low-high-low" wave trains at 500 hPa height are observed in the middle and high latitudes in the WH-EA pattern at 500 hPa height, meaning precipitation increases in WNWC and decreases in ENWC. Thus, the atmosphere circulation situation with two wave train types can support both the precipitation seesaw phenomenon and the opposite variation between MI and WI. Moreover, the seesaw phenomenon is shown to be related to the separate or joint effects of the South Asian High, ENSO and the plateau heating on the common but opposite effect on the summer monsoon and the westerlies, in which the South Asian High probably plays a more critical role. This study could deepen the scientific understanding of precipitation mechanisms and improve the weather forecast technology in Northwest China during the flood season. [ABSTRACT FROM AUTHOR]

The development of remote sensing has enabled the acquisition of information on land-cover change at different spatial scales. However, a tradeoff between spatial and temporal resolutions normally exists. Fine-spatial-resolution images have low temporal resolutions, whereas coarse-spatial-resolution images have high temporal repetition rates. A novel superresolution change detection method (SRCD) is proposed to detect land-cover changes at both fine spatial and temporal resolutions with the use of a coarse-resolution image and a fine-resolution land-cover map acquired at different times. SRCD is an iterative method that involves endmember estimation, spectral unmixing, land-cover fraction change detection, and superresolution land-cover mapping. Both the land-cover change/no-change map and from-to change map at fine spatial resolution can be generated by SRCD. In this paper, SRCD was applied to a synthetic multispectral image, a Moderate-Resolution Imaging Spectroradiometer multispectral image, and a Landsat-8 Operational Land Imager multispectral image. The land-cover from-to change maps are found to have the highest overall accuracy (higher than 85%) in all of the three experiments. Most of the changed land-cover patches, which were larger than the coarse-resolution pixel, were correctly detected. [ABSTRACT FROM AUTHOR]

Extreme climate events are hotspots in global change. However, research on the changes in future compound events and population exposure is still limited. Leveraging from the data of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), this paper aims to analyze the temporal and spatial changes of global compound temperature and precipitation extreme events in the future. We also predict the risk of population exposure and quantify the contribution of different factors to exposure. The results show that: (1) In the next 80 years, compound hot-dry event (CHDE) will increase at a rate of 0.02, 0.03, and 0.08 days per decade under the three scenarios of SSP1–2.6, SSP2–4.5 and SSP5–8.5, respectively. By comparison, compound hot-humidity event (CHHE) shows a downward trend under the three scenarios, with a downward rate of 0.01, 0.02, and 0.11 days per decade, respectively. (2) Under the SSP1–2.6 and SSP2–4.5 scenarios, CHDE and CHHE have two or more mutation points. Under the SSP5–8.5 scenario, CHDE shows a significant upward and CHHE shows a significant downward trend in the middle and late 21st century. These two indices exhibit periodic changes in all three scenarios (3) South Asia, West Asia, and Northeast Africa have higher CHDE values, while regions with higher CHHE values are located in North Asia and Greenland. (4) Climate change is a major factor affecting population exposure. For CHDE, climate, population, and their synergistic effects contribute about 75%, 20%, and 5% to the exposure, respectively. For CHHE, the contributions of these three factors are 85%, 10%, and 5% respectively. These findings provide scientific guidance for the rational formulation of population policies, the effective avoidance of climate disaster risks and the protection of human health. • Frequency of hot-dry events and hot-humidity events will increase and decrease, respectively, in the future. • There are abrupt points and periodicity in the future time series of compound temperature and precipitation extremes. • Asia is projected to have the greatest exposure and climate change contributes the most to exposure. [ABSTRACT FROM AUTHOR]

Dimethyl sulfide (DMS), an organic volatile sulfide produced from Dimethylsulfoniopropionate (DMSP), exerts a significant impact on the global climate change. Utilizing published literature data spanning from 2005 to 2020, a BP neural network (BPNN) model of the surface seawater DMS in the Yellow and East China Sea (YECS) was developed to elucidate the influence of various marine factors on the DMS cycle. Results indicated that the six parameters inputted BPNN model, that include the time (month), latitude and longitude, sea-surface chlorophyll a (Chl-a), sea-surface temperature (SST), and sea-surface salinity (SSS), yielded the optimized simulation results (R2 = 0.71). The optimized estimation of surface seawater DMS in the YECS were proved to be closely aligned with the observed data across all seasons, which demonstrated the model’s robust applicability. DMS concentration in surface seawater were found to be affected by multiple factors such as Chl-a and SST. Comparative analysis of the three environmental parameters revealed that Chl-a exhibited the most significant correlation with surface seawater DMS concentration in the YECS (R2 = 0.20). This underscores the pivotal role of chlorophyll in phytoplankton photosynthesis and DMS production, emphasizing its importance as a nonnegligible factor in the study of DMS and its sulfur derivatives. Furthermore, surface seawater DMS concentration in the YECS exhibited positive correlations with Chl-a and SST, while displaying a negative correlation with SSS. The DMS concentration in the YECS show substantial seasonal variations, with the maximum value (5.69 nmol/L) in summer followed in decreasing order by spring (3.96 nmol/L), autumn (3.18 nmol/L), and winter (1.60 nmol/L). In the YECS, there was a gradual decrease of DMS concentration from the nearshore to the offshore, especially with the highest DMS concentration concentrated in the Yangtze River Estuary Basin and the south-central coastal part off the Zhejiang Province. Apart from being largely composed by the release of large amounts of nutrients from anthropogenic activities and changes in ocean temperature, the spatial and temporal variability of DMS may be driven by additional physicochemical parameters. [ABSTRACT FROM AUTHOR]

The Earth's climate is largely determined by its energy budget. Since the 1960s, satellite remote sensing has been used in estimating these energy budget components at both the top of the atmosphere (TOA) and the surface. Besides the broadband sensors that have been traditionally used for monitoring Earth's Energy Budget (EEB), data from a variety of narrowband sensors aboard both polar-orbiting and geostationary satellites have also been extensively employed to estimate the EEB components. This paper provides a comprehensive review of the satellite missions, state-of-the art estimation algorithms and the satellite products, and also synthesizes current understanding of the EEB and spatio-temporal variations. The TOA components include total solar irradiance, reflected shortwave radiation/planetary albedo, outgoing longwave radiation, and energy imbalance. The surface components include incident solar radiation, shortwave albedo, shortwave net radiation, longwave downward and upwelling radiation, land and sea surface temperature, surface emissivity, all-wave net radiation, and sensible and latent heat fluxes. Some challenges, and outlook such as virtual constellation of different satellite sensors, temporal homogeneity tests of long time-series products, algorithms ensemble, and products intercomparison are also discussed. [ABSTRACT FROM AUTHOR]

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In order to explore the evolvement mechanism of hydrological elements under climate change and the interaction impacts, the hydro-meteorological elements, like temperature, precipitation, runoff, and evaporation, are studied by statistical analysis method and cause analysis approach in this paper. Initially, in terms of climate change across Anhui province, the average temperature data from 1960 to 2009 at 15 meteorological stations are selected from China Meteorological Data Sharing Network to analyze the spatio-temporal variation of temperature characteristics. Secondly, from the perspective of Xin'anjiang Basin, evolution rules of hydro-meteorological elements are studied through deviation variation of every 10 years and the long-term development trend from 1981 to 2010 at 9 hydrological stations. And the influence of climate change on runoff is analyzed according to the principle of runoff formation, and the response of runoff to precipitation and temperature is predicted with the simulating future climate change by climate models. The results show that, through statistics, the average temperature in Anhui province is in a fluctuating downward state from 1960 to 1990. After 1990, the uptrend is significant. In Xin'anjiang Basin, the annual precipitation declines overally, especially at some stations it is obvious. The variation of runoff is in a downtrend consistent with precipitation, but it is not noticeable, taken as a whole. The average evaporation changes unremarkably, except in summer and winter. Under three scenarios of A1B, A2 and B1, it is assumed that temperature, precipitation and runoff are all increasing in the future. Through cause analysis, these mainly seem to be affected by the circulation of weather background and sea surface temperature. Based on the analysis of hydro-meteorological elements under climate change and the forecasting of their future changes, the development of regional water resources can be grasped which is of great significance for human interests to water supply scheme, water conservancy project planning, and the economic-social sustainablility. [ABSTRACT FROM AUTHOR]

In this paper, we quantify the changes in precipitation distribution in south-western Poland between the periods 1891-1930 and 1981-2010. The average monthly precipitation totals available for 368 and 245 stations, respectively, are spatially interpolated and processed to calculate maps of differences and pluvial continentality indices for both periods. The maps are analysed against changes in atmospheric circulation and take into account the potential role of the mountain barrier of the Sudetes. The main findings are (1) winter totals generally increased in conjunction with a higher frequency of zonal circulation; it is more pronounced in the upper parts of the Sudetes due to orographic effects; (2) a decreased frequency of zonal circulation together with an increased frequency of southern flows has likely led to the reduction of precipitation in spring and autumn. These changes have led to a significant modification of the region's pluvial regime, mostly by decreasing continentality features (especially in the Sudetes and the western lowlands). In addition, the strong sheltering influence of the mountain barrier is observed, driving the preservation or even the enhancement of the continental pluvial characteristics in the north-eastern to eastern foregrounds of the Sudetes. This leads to the conclusion that in areas where long-term observed trends or future projections of precipitation are ambiguous, there may occur significant modifications in regional characteristics, especially as a result of changes in atmospheric circulation modified by local orography. [ABSTRACT FROM AUTHOR]

This paper provides practical experience with two climate impact and adaptation assessment methods: Real- In- Options ( RIO) optimisation and Adaptation Tipping Points ( ATP). These methods were selected because they both provide insight into and promote the ability of the system to deal with future change and thus can be used within a resilience approach. The resilience approach takes a dynamic perspective on adaptive processes and the effects of these processes at/across different spatio-temporal scales. Although the two methods share a similar aim, they have considerable differences in orientation and application. RIO optimisation aims to minimise the expected costs of acquiring climate change resilience. To achieve this aim, it uses probabilistic climate data to identify the optimal set of adaptive strategies in response to advances in knowledge about future climate change. The ATP method is virtually independent of climate change scenarios, and in particular of probabilities of climate change. Rather, it requires input from decision makers and other stakeholders to select the preferred adaptive strategy. This paper discusses the concept, procedures, case examples and benefits/limitations of each method, examining its usefulness for informing adaptation-related decision making. Based on this, it gives specific recommendations on which method to use under what circumstances. [ABSTRACT FROM AUTHOR]

Changes in climatic conditions, together with urban population growth, are making the development of tools to help disaster planners and policy makers select mitigation and adaptation measures a priority. The Coastal Cities at Risk (CCaR) project is a multidisciplinary team project involving four large coastal cities: Manila (The Philippines), Bangkok (Thailand), Lagos (Nigeria) and Vancouver (Canada). One of the project objectives includes development of a system dynamics simulation model to assess the resilience of the participating cities to climate change caused by sea level rise and riverine flooding. The resilience model is designed to integrate physical, economic, health, social and organizational impacts of climate change. This paper presents the methodology for providing spatial and temporal information on climate change health impacts for use in the resilience simulator. Basic population data, disease burden and physical conditions are integrated in the development of a composite health impact map. The output of this mapping exercise provides a more fully integrated view of population health to allow for the development of more targeted adaptive and risk reduction strategies at a local level for the City of Metro Vancouver. This methodology has been applied using data for 3 years, 2001, 2006 and 2011 in order to give a dynamic simulation of health impacts using the resilience simulator. The final maps indicate that the Richmond and Delta regions of Metro Vancouver are more vulnerable to climate change caused by sea level rise and flooding compared to other municipalities. The paper demonstrates how composite health impact maps can be used both as an input for resilience modeling, as well as a 'stand-alone' product for the assessment and development of mitigation and adaptive strategies for coastal cities. [ABSTRACT FROM AUTHOR]

Global warming induced by atmospheric CO2 has attracted increasing attention of researchers all over the world. Although space-based technology provides the ability to map atmospheric CO2 globally, the number of valid CO2 measurements is generally limited for certain instruments owing to the presence of clouds, which in turn constrain the studies of global CO2 sources and sinks. Thus, it is a potentially promising work to combine the currently available CO2 measurements. In this study, a strategy for fusing SCIAMACHY and GOSAT CO2 measurements is proposed by fully considering the CO2 global bias, averaging kernel, and spatiotemporal variations as well as the CO2 retrieval errors. Based on this method, a global CO2 map with certain UTC time can also be generated by employing the pattern of the CO2 daily cycle reflected by Carbon Tracker (CT) data. The results reveal that relative to GOSAT, the global spatial coverage of the combined CO2 map increased by 41.3% and 47.7% on a daily and monthly scale, respectively, and even higher when compared with that relative to SCIAMACHY. The findings in this paper prove the effectiveness of the combination method in supporting the generation of global full-coverage XCO2 maps with higher temporal and spatial sampling by jointly using these two space-based XCO2 datasets. [ABSTRACT FROM AUTHOR]

Bad weather and rough seas continue to be a major cause for ship losses and is thus a significant contributor to the risk to maritime transportation. This stresses the importance of taking severe sea state conditions adequately into account, with due treatment of the uncertainties involved, in ship design and operation in order to enhance safety. Hence, there is a need for appropriate stochastic models describing the variability of sea states. These should also incorporate realistic projections of future return levels of extreme sea states, taking into account long-term trends related to climate change and inherent uncertainties. The stochastic ocean wave model presented in this paper exploits the flexible framework of Bayesian hierarchical space-time models. It allows modelling of complex dependence structures in space and time and incorporation of physical features and prior knowledge, yet at the same time remains intuitive and easily interpreted. Furthermore, by taking a Bayesian approach, the uncertainties of the model parameters are also taken into account. A regression component with $$\text{ CO }_2$$ as an explanatory variable has been introduced in order to extract long-term trends in the data. The model has been fitted by monthly maximum significant wave height data for an area in the North Atlantic ocean. The different components of the model will be outlined in the paper, and the results will be discussed. Furthermore, a discussion of possible extensions to the model will be given. [ABSTRACT FROM AUTHOR]

Adaptation to climate change is becoming a prominent issue in both landscape research and land-use planning. Current research focuses mainly on the description of potential impacts for different societal sectors and in general fails to provide useful information to help define climate adaptation strategies and specific policy measures or development plans. This editorial briefly explores the reasons why this may be the case and proposes a conceptual framework for more effective climate adaptation research. Furthermore, it introduces three papers that address adaptation of landscape systems to climate change as an integrated multi-level challenge. The included papers focus on the relationship between climate-induced changes in the natural system and the economics-oriented societal system and specifically address the interdependencies across scales. [ABSTRACT FROM AUTHOR]

The present century i.e. 21st Century is the century of Business and Climate. These two issues are getting higher attention from both developed countries and developing economies. On the one hand, business has affecting environment and on the other hand environmental products are boosting business and therefore, there is close relationship between business and climate change Looking into the growing grave concern from different sections over climate change, considerably due to industrial units, businesses had also started to take initiative in this regard. The paper, henceforth, concentrates on what had so far been done, and what more need to be done for maintaining ecological balance. The paper, as such, is divided into four parts. Section second briefly brings strategic information on climate change. Section third of the study traces-out distinct problems and barriers associated to climate change, particularly in Indian scenario. Finally section four of the study lays down various solutions for tackling the problem of climate change. [ABSTRACT FROM AUTHOR]

Plant phenology is the most salient and sensitive indicator of terrestrial ecosystem response to climate change. Studying its change is significantly important in understanding and predicting impressively changes in terrestrial ecosystem. Based on NDVI from SPOT VGT, this paper analyzed the spatiotemporal changes in alpine grassland phenology in Qinghai-Tibetan Plateau from 1999 to 2009. The results are enumerated as follows: (1) The spatial distribution of the average alpine grassland phenology from 1999 to 2009 is closely related to water and heat conditions. Accompanying the deterioration in heat and water conditions from southeast to northwest, the start of growth season (SOG) was delayed gradually, the end of growth season (EOG) advanced slowly, and the length of growth season (LOG) shortened gradually. Elevation played an important role in the regional differentiation of phenology, but a dividing line of approximately 3500 m existed. Below this line, the phenology fluctuated irregularly with altitude change, whereas above the line, the phenology is closely related to altitude change. (2) From 1999 to 2009, SOG of the alpine grassland came earlier by six days per decade ( R=0.281, P=0.093), EOG was late by two days per decade ( R=0.031, P=0.605), and LOG lengthened by eight days per decade ( R=0.479, P=0.018). The early SOG, the late EOG, and the extended LOG mainly occurred at the center and east of the Plateau. SOG in most of the Plateau advanced significantly, especially in the eastern Plateau. (3) The inter-annual phenology changes of the alpine grassland in the Qinghai-Tibetan Plateau exhibited significant differentiation at different elevation and natural zones. The inter-annual changes at high altitude were more complicated than that at low altitude. The most significant phenology changes were found in the eastern Qinghai-Qilian montane steppe zone, and non-significant changes occurred in the Southern Tibet montane shrub-steppe zone. [ABSTRACT FROM AUTHOR]

Recent advances in the study of the characteristics, processes, and causes of spatio-temporal variabilities of the East Asian monsoon (EAM) system are reviewed in this paper. The understanding of the EAM system has improved in many aspects: the basic characteristics of horizontal and vertical structures, the annual cycle of the East Asian summer monsoon (EASM) system and the East Asian winter monsoon (EAWM) system, the characteristics of the spatio-temporal variabilities of the EASM system and the EAWM system, and especially the multiple modes of the EAM system and their spatio-temporal variabilities. Some new results have also been achieved in understanding the atmosphere-ocean interaction and atmosphere-land interaction processes that affect the variability of the EAM system. Based on recent studies, the EAM system can be seen as more than a circulation system, it can be viewed as an atmosphere-ocean-land coupled system, namely, the EAM climate system. In addition, further progress has been made in diagnosing the internal physical mechanisms of EAM climate system variability, especially regarding the characteristics and properties of the East Asia-Pacific (EAP) teleconnection over East Asia and the North Pacific, the “Silk Road” teleconnection along the westerly jet stream in the upper troposphere over the Asian continent, and the dynamical effects of quasi-stationary planetary wave activity on EAM system variability. At the end of the paper, some scientific problems regarding understanding the EAM system variability are proposed for further study. [ABSTRACT FROM AUTHOR]

Because of its tremendous temporal and spatial scope, climate change poses profound regulatory issues. Significant transboundary effects and spatially differentiated effects make it highly desirable that international regulatory mechanisms are utilised in order to arrive at effective mitigation and adaptation solutions. Yet, the different spaces that states occupy in terms of causation and effect makes agreement on what must be regulated through international mechanisms and indeed how to regulate such subject matter. Consequently, this paper proposes that legitimacy needs to be considered one of the core concerns of international climate change regulation and governance. The aim of this paper is to clarify the role of the concept of legitimacy in international climate change regulation, and to set forth a specific discursive approach aimed at identifying legitimacy-enhancing design features for internationally regulating climate change. [ABSTRACT FROM AUTHOR]

This opinion piece examines Weaver's thesis that sustainable tourism's current expanding engagement with climate change may not necessarily be conducive to the interests of tourism sustainability. It critically examines and responds to the seven interrelated issues presented by Weaver to support that opinion. This paper dispels some common climate science myths that continue to hamper scientific progress and obfuscate debate over climate change policy responses and specifically refutes recent claims of compromised and inaccurate research findings. It is argued that climate change studies reveal a deficiency in past conceptualizations of sustainable tourism that focused almost exclusively on destination scale issues and highlight the need to properly account for the environmental and social impacts of tourism's travel phase. Addressing climate change is considered a prerequisite to sustainable development and therefore germane to advancing sustainable tourism research. Tourism is currently considered among the economic sectors least prepared for the risks and opportunities posed by climate change and is only now developing the capacity to advance knowledge necessary to inform business, communities and government about the issues and potential ways forward. Any retreat from engagement with climate change issues by the tourism industry or its researchers would be to their substantial detriment. [ABSTRACT FROM AUTHOR]

Meteorological and oceanographic data from ships of opportunity are the largest contributor to the world’s ocean surface database and thus are extensively used to estimate the change in climatic properties over the world’s oceans during the previous 150 years. The importance of these data for climate change studies underscores the need to fully understand the error associated with averages of these data. The sampling error problem is especially acute for ship data due to the fact that ships are moving platforms and, thus, report observations from constantly varying locations with time. This paper develops a theoretical framework for assessing the averaged sampling error associated with monthly, 1°×1° latitude-longitude box averaged ship data. It should be noted that the time-space distribution of ships within the averaging domain strongly affects the sampling error. This is shown in our derivation. The framework developed here can be used to improve upon existing methods for estimating the sampling error associated with three-dimensional box averages of meteorological and oceanographic data obtained from ship records. The framework is complimentary to existing methods of assessing biases and random error due to instrumentation, recording, etc. It is demonstrated mathematically that the uncertainty due to incomplete sampling is primarily a trade off between of the number of observations and their relative locations within the box as well as the inherent time-space correlation structure of the variable of interest. This work differs from other studies in that the three-dimensional interdependence of data is taken into account in deriving an expression for the sampling error. [ABSTRACT FROM AUTHOR]

There is confusion regarding the notion of “vulnerability” in the climate change scientific community. Recent research has identified a need for formalisation, which would support accurate communication and the elimination of misunderstandings that result from the use of ambiguous terminology. Moreover, a formal framework of vulnerability is a prerequisite for computational approaches to its assessment. This paper presents an attempt at developing such a formal framework. We see vulnerability as a relative concept in the sense that accurate statements about vulnerability are possible only if one clearly specifies (1) the entity that is vulnerable, (2) the stimulus to which it is vulnerable and (3) the preference criteria to evaluate the outcome of the interaction between the entity and the stimulus. We relate the resulting framework to the Intergovernmental Panel on Climate Change conceptualisation of vulnerability and two recent vulnerability studies. [ABSTRACT FROM AUTHOR]

Significant changes in the climatic system have been observed, which may be attributed to human-enhanced greenhouse effect. Even stronger changes are projected for the future, impacting in an increasing way on human activity sectors. The present contribution, prepared in the framework of the MICE (Modelling the Impact of Climate Extremes) Project of the European Union, reviews how climate change may impact on winter tourism in the Alpine region, intense precipitation and flood potential in central Europe, forest damage in Scandinavia and beach holidays in the Mediterranean coast. Impacts are likely to be serious and largely adverse. Due to a lack of adequate information and lack of broadly accepted and reliable mathematical models describing the impact of changes in climate extremes on these activity sectors, it has been found useful to use expert judgement based impact assessment. Accordingly, regional mini-workshops were organized serving as platforms for communication between scientists and stakeholders, vehicles for dissemination of the state-of-the-art of the scientific understanding and for learning stakeholders’ view on extreme events, their impacts and the preparedness system. Stakeholders had the opportunity to react to the scientific results and to reflect on their perception of the likely impacts of projected changes in extremes on relevant activity sectors and the potential to adapt and avert adverse consequences. The results reported in this paper present the stakeholders’ suggestions for essential information on different extreme event impacts and their needs from science. [ABSTRACT FROM AUTHOR]

Liu, W.; Feng, Q., and Zhang, F., 2017. Spatial-temporal variation characteristics of NPP in the Heihe River basin, Northwestern China, in a recent 10-year period calculated by CASA model. In: Zhi, Y. and Guido Aldana, P.A. (eds.), Sustainability of Water Resources and the Development of Coastal Environments: Select Proceedings from the 2016 International Conference on Water Resource and Environment (WRE2016). Journal of Coastal Research, Special Issue No. 80, pp. 36-47. Coconut Creek (Florida), ISSN 0749-0208. In this paper, the spatial-temporal variation characteristics of NPP and related factors in the Heihe River basin were analyzed using the meteorological data of the Heihe River basin, SPOT/NDVI data, and other modified mathematical statistical methods. The results show that during the 1999-2010 period the total NPP in the Heihe River basin exhibited a fluctuating increasing trend, reaching 0.3×1012 gC/a. The multiyear variation value of NPP was 0.19~601.09 gC/m2/a in the whole basin, 3.5 gC/m2/a in the upper reaches, 2.06 gC/m2/a in the middle reaches, and 0.31 g/m2/a in the lower reaches. The NPP of vegetation in the Heihe River basin during January and February tended to slightly decrease, and on average varied between −0.01 and −0.02 gC/m2/a, while in June, July, August, and September greatly increased. The NPP of 10 vegetation types exhibited an increasing trend in 2010 compared to 1999, on average increased by 21.02 gC/m2/a. Among various vegetation types the increase in the NPP value of swamp vegetation was the largest, reaching 46.24 gC/m2/a followed by planted vegetation, steppe types, and meadow types; there values were 39.22 gC/m2/a, 32.49 gC/m2/a, and 27.63 gC/m2/a, respectively. The accumulated NPP in grassland ecosystem in the Heihe River basin was the largest, which is of important significance to the eco-environment of the basin. As viewed for the whole Heihe River basin, the yearly mean cumulative value of NPP of vegetation was negatively related to annual mean temperature and annual total solar radiation but positively related to annual total precipitation, suggesting that water is a limiting factor to the NPP in the Heihe River basin. [ABSTRACT FROM AUTHOR]

The estimation of drought at certain temporal and spatial scales is useful for research on climate change and global warming. Greece is often affected by droughts, which are widespread spatially and temporally due to the complex topography. Within the Greek territory, various complex microclimates are created, linked with the spatial variances in drought phenomena. In this paper an estimation of drought in the Sperchios River basin was conducted using the Aridity Index (AI). Additionally, a seasonal analysis of drought was performed. Meteorological data from the Hellenic National Meteorological Service (HNMS) were used as inputs for the AI equation. Spatial interpolation of AI for the Sperchios River basin was performed using a kriging method by the application of ArcGIS 9.3. In order to produce required input data, several models (EmPEst, RayMan) and techniques (linear regression, interpolation) were combined. Finally, the meteorological data series were randomly separated into two periods and AI was estimated for these sub-periods, in order to test the effectiveness of the drought index used. The results indicate that the conditions prevailing in the area are humid, mostly affected by increased rainfall occurring in the mountainous section of the basin. Broadly, the humid environment in the upstream of Sperchios River prevents drought occurring in the lowlands of Sperchios River valley. Nevertheless, some differentiation appeared during the summer period, to which special attention needs to be given in order to prevent drought conditions.Editor Z. W. Kundzewicz Associate editor not assigned [ABSTRACT FROM AUTHOR]

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The scan statistic is a popular choice for monitoring and detecting spatio-temporal outbreaks. It can be designed to be optimal if the outbreak characteristics (shape and size) are known in advance. However, in all practical situations, neither the shape nor the size are known in advance. Therefore, there is a need for spatio-temporal surveillance plans that perform well for a range of unknown outbreaks. This paper proposes a new approach for detecting spatio-temporal outbreaks based on the cumulative sum of order statistics. The approach performed on average better than the simple scan statistic for both a range of outbreaks involving a single geographical region. More importantly, it performed significantly better than the simple scan plan for outbreaks involving simultaneous multiple (non-overlapping) geographically dispersed regions. [ABSTRACT FROM AUTHOR]

The variation of polar sea ice is an indicator of polar environmental change, which plays an important role in the study of regional and global climate change. In this paper, the latest sea ice data sets from the European Space Agency Climate Change Initiative were firstly combined to comprehensively analyze the spatial and temporal variation of Arctic sea ice concentration (SIC) and sea ice thickness (SIT) from 2002 to 2017. The results show that during this period, the SIC of the Kara Sea and the Barents Sea decreases the most significantly, reaching −1.11%·year−1. The Arctic annual average sea ice extent (SIE) and sea ice area (SIA) exhibit noticeable decreasing trends, reaching −0.0592 × 106 km·year−1 and −0.0628 × 106 km·year−1, respectively. Both of Arctic SIE and SIA vary seasonally, and they reach the minimum values of 4.97 × 106 km2 and 3.96 × 106 km2 in September and the maximum values of 14.58 × 106 km2 and 13.35 × 106 km2 in March, respectively. Moreover, the Arctic SIE and SIA anomaly series from 2002 to 2017 were analyzed based on the linear regression method. It is found the most noticeable decreasing trends of the Arctic SIE and SIA are from July to October. The maximum variation of the SIE is −1.06 × 106 km2(10a)−1, and the maximum variation of the SIA is −1.09 × 106 km2(10a)−1. The Arctic SIE and SIA present the smallest decreasing trends from April to May, which are −0.19 × 106 km2(10a)−1 and −0.2 × 106 km2(10a)−1, respectively. The Arctic SIT also displays a pronounced decreasing trend with −0.015 m·year−1 in autumn and −0.018 m·year−1 in winter. The annual average SIT reaches a minimum of 1.28 m in 2013 and a maximum of 1.66 m in 2005. Particularly, the SIT of the Hudson Bay decreases the most significantly, reaching −0.051 m·year−1. Finally, the Arctic average SIT was tested by the Mann‐Kendall method. It is found that the SIT has a sudden change in 2007, from a slight increase in 2003–2007 to a significant decrease in 2007–2017. Overall, the decreasing trend of the Arctic sea ice (both SIC and SIT) is significant in the past decade. These findings can contribute to future research on polar regions and global climate change. Key Points: We analyzed the whole and regional variation of Arctic sea ice by combining use of the latest sea ice concentration and sea ice thickness data sets from European Space AgencyThe sea ice concentration in the Barents Sea and the Kara Sea and the sea ice thickness in the Hudson Bay decreased the most obviouslyThe sea ice thickness has a sudden change in 2007 based on Mann‐Kendall mutation test [ABSTRACT FROM AUTHOR]

Most individuals living in low-income households in cities like Dhaka, Bangladesh, reside in informal, high-density settlements that experience exacerbated vulnerabilities from climate change. When determining development approaches that take into account future needs for adaptation, community residents and the professionals and policymakers who support these communities must consider the existing challenges associated with living in poverty, the rapidly increasing rate of urbanization, and future climate risks. The key objectives of this paper are to expand the understanding of the co-evolution of these “wicked problems” and the need for developing alternative strategies for adaptation. The paper explores the organic creativity of residents living in informal settlements as they draw upon locally available resources to create situation specific, pragmatic solutions to locally defined challenges. We term this practice “fluid engagement,” a design approach that aspires to be flexible, situated, pragmatic and participative, and demonstrates a willingness to be open to heterogeneous combinations of purpose and programs. Critically, “fluidity” suggests a participative approach to design that takes into account the “voices” beyond the policy and professional nexus to the participation of the community. This paper aims to connect emerging literature concerning the urban practices of low-income households in cities of the South with a detailed case-study analysis of the spatial practices at the micro-level in an informal settlement in Dhaka. The paper contributes to a new agenda of architectural and urban-development research that engages with issues of poverty and climate change as a process of co-evolution. [ABSTRACT FROM AUTHOR]

Climate change in North China would result in significant changes in temperature, precipitation and their spatial/temporal distributions. Consequently, these induced changes will have profound effects on the hydrological cycle and water resources in both agricultural and natural ecosystems. Panjiakou reservoir in the middle Luanhe River basin-a tributary of the Haihe River basin-is one of the important sources of water for industrial and agricultural development in Beijing, Tianjin and Hebei province, China. Any significant change in the magnitude and/or timing of runoff from the reservoir induced by changes in climatic variables would have significant implication for the economic prosperity in North China. This paper investigates the impacts of climate change on hydrological processes in the Luanhe River basin as follows. Firstly, spatial and temporal patterns of precipitation, temperature and runoff at both annual and seasonal scales from 1957 to 2000 in the Luanhe River basin are analyzed using Mann-Kendall trend analysis, linear regression methods and inverse distance weighted interpolation. For the impact study, four Global Climate Models (GCMs) (named CSIRO, HadCM3, CNRM and GFDL) were used to produce precipitation and temperature data under A2 scenario by mean of a widely used quantile-quantile transformation. Projected meteorological variables were used to force a two-parameter hydrologic model to simulate the hydrological response to climate change in the future (2021-2050). Moreover, a sensitivity analysis is conducted to assess how precipitation and temperature affect the runoff. Results suggested that most part of the Luanhe River basin was dominated by significant increasing trends of temperature and no significant trends of precipitation in annual and seasonal scale during the past decades. Annual, spring and autumn runoffs present significant decreasing trends in the Panjiakou reservoir basin. Meanwhile, runoff is more strongly related to precipitation than to temperature. All GCMs projected precipitation and temperature series after bias correction indicated increasing temperature and increasing precipitation trends for the period 2021-2050 except that CNRM showed a slight decreasing trend in precipitation. Great enhancements can be found in projected runoff except CNRM by driving the two-parameter water balance model. The study provides valuable information on the assessment of the impact of the climate change on water resources in the Luanhe River basin as well for allocating and designing water resources projects. [ABSTRACT FROM AUTHOR]

Downscaled regional climate simulations for Fiji and Federated States of Micronesia (FSM) from Coupled Model Intercomparison Project 3 (CMIP3) Global Coupled Models (GCMs) are carried out as part of the Pacific Climate Change Science Program (PCCSP) using the CSIRO stretched-grid Conformal Cubic Atmospheric Model (CCAM) for the period 1980-2000. The model simulations are performed at 60 km and at 8 km horizontal resolutions. The higher resolution is chosen to partly resolve the detailed orography of the countries selected, providing insight into the spatial distribution of temperature and precipitation for an individual island that GCMs are unable to represent. The downscaling is carried out in two steps. First, CCAM simulations are run on a global 60 km grid using bias-corrected sea surface temperatures (SST) and sea ice from six CMIP3 GCMs. Three of these 60 km simulations are then further downscaled to 8 km resolution over the two countries. The focus of this paper is validation of these high-resolution simulations against gridded analyses, satellite rainfall climatologies and available station data for rainfall and temperature for the period 1980-2000. The results show that high resolution orography plays an important role in capturing the current climate. The validation demonstrates that the 8 km simulations are capable of realistically depicting the current climate when the topography influences the wind flow and rainfall (Fiji), performing better than both the GCMs and the 60 km downscaled simulations. However, the performance of the 8 km simulations is similar to the GCM and 60 km simulations when topographical features are not present (FSM). [ABSTRACT FROM AUTHOR]

1. Anthropogenic global change compromises forest resilience, with profound impacts to ecosystem functions and services. This synthesis paper reflects on the current understanding of forest resilience and potential tipping points under environmental change and explores challenges to assessing responses using experiments, observations and models. 2. Forests are changing over a wide range of spatio-temporal scales, but it is often unclear whether these changes reduce resilience or represent a tipping point. Tipping points may arise from interactions across scales, as processes such as climate change, land-use change, invasive species or deforestation gradually erode resilience and increase vulnerability to extreme events. Studies covering interactions across different spatio-temporal scales are needed to further our understanding. 3. Combinations of experiments, observations and process-based models could improve our ability to project forest resilience and tipping points under global change. We discuss uncertainties in changing CO2 concentration and quantifying tree mortality as examples. 4. Synthesis. As forests change at various scales, it is increasingly important to understand whether and how such changes lead to reduced resilience and potential tipping points. Understanding the mechanisms underlying forest resilience and tipping points would help in assessing risks to ecosystems and presents opportunities for ecosystem restoration and sustainable forest management. [ABSTRACT FROM AUTHOR]

Background Over the last decade, the incidence of confirmed malaria has declined significantly in Nepal. The aim of this paper is to assess the spatio-temporal distribution of malaria and its association with climatic factors and vector control interventions in two high-risk districts of Nepal. Methods Hotspot analysis was used to visualize the spatio-temporal variation of malaria incidence over the years at village level and generalized additive mixed models were fitted to assess the association of malaria incidence with climatic variables and vector control interventions. Results Opposing trends of malaria incidence were observed in two high-risk malaria districts of eastern and far-western Nepal after the introduction of long-lasting insecticidal nets (LLINs). The confirmed malaria incidence was reduced from 2.24 per 10,000 in 2007 to 0.31 per 10,000 population in 2011 in Morang district but increased from 3.38 to 8.29 per 10,000 population in Kailali district. Malaria hotspots persisted mostly in the same villages of Kailali district, whereas in Morang district malaria hotspots shifted to new villages after the introduction of LLINs. A 1° C increase in minimum and mean temperatures increased malaria incidence by 27% (RR =1.27, 95% CI =1.12-1.45) and 25% (RR =1.25, 95% CI =1.11-1.43), respectively. The reduction in malaria incidence was 25% per one unit increase of LLINs (RR =0.75, 95% CI =0.62-0.92). The incidence of malaria was 82% lower in Morang than in Kailali district (RR =0.18, 95% CI =0.11-0.33). Conclusions The study findings suggest that LLIN coverage should be scaled up to entire districts rather than high-incidence foci only. Climatic factors should be considered for malaria microstratification, mosquito repellents should be prescribed for those living in forests, forest fringe and foothills and have regular visits to forests, and imported cases should be controlled by establishing fever check posts at border crossings. [ABSTRACT FROM AUTHOR]

The carbon balance of terrestrial ecosystems is intertwined with climate and changes in land use. Over the past 30 years, the Loess Plateau (LP) has experienced temperature increases and an expansion of forest and grassland. The net ecosystem productivity (NEP) underlying these changes is worth investigating. Using three periods (i.e., 1990–2000, 2000–2010, and 2010–2019) of annual average NEP and climatic, topographic, and land use data, we analyzed changes in the spatial distribution of carbon sources and sinks of the LP. Using an optimal parameter-based geographical detector model to discuss the driving factors of carbon sources and sinks, we found that: (1) The area of carbon sinks has been increasing continuously, and that the distributions of both of these elements are zonal. The carbon sinks show a downward trend from south to north, which is mainly driven by climate and land use type. (2) Carbon sources are mainly concentrated in the middle temperate zone, and they are mainly linked to impervious land, unused land, and grassland. The carbon sinks are mainly concentrated in the south temperate zone and plateau climatic zone, and they are mainly linked to forest, grassland, and cultivated land. Additionally, the southern temperate zone has been the most green, due to its superior hydrothermal conditions that sustain carbon sinks. (3) It is not uncommon for some forests, grasslands, and cultivated land to transition between being carbon sources and carbon sinks, especially when affected by human intervention and inadequate management measures. (4) NEP was primarily influenced by CO2 concentration, temperature, and precipitation, and the interaction of these factors greatly influenced the dynamics of carbon sources and sinks, while terrain exerted insignificant impacts on the NEP. This study highlights the importance of the carbon balance in terrestrial ecosystems and can be used to guide the creation of vegetation-based carbon sequestration policies. [ABSTRACT FROM AUTHOR]

Land-use and land-cover ( LULC) change is one of the largest drivers of biodiversity loss and carbon emissions globally. We use the tropical rainforests of the Amazon, the Congo basin and South-East Asia as a case study to investigate spatial predictive models of LULC change. Current predictions differ in their modelling approaches, are highly variable and often poorly validated. We carried out a quantitative review of 48 modelling methodologies, considering model spatio-temporal scales, inputs, calibration and validation methods. In addition, we requested model outputs from each of the models reviewed and carried out a quantitative assessment of model performance for tropical LULC predictions in the Brazilian Amazon. We highlight existing shortfalls in the discipline and uncover three key points that need addressing to improve the transparency, reliability and utility of tropical LULC change models: (1) a lack of openness with regard to describing and making available the model inputs and model code; (2) the difficulties of conducting appropriate model validations; and (3) the difficulty that users of tropical LULC models face in obtaining the model predictions to help inform their own analyses and policy decisions. We further draw comparisons between tropical LULC change models in the tropics and the modelling approaches and paradigms in other disciplines, and suggest that recent changes in the climate change and species distribution modelling communities may provide a pathway that tropical LULC change modellers may emulate to further improve the discipline. Climate change models have exerted considerable influence over public perceptions of climate change and now impact policy decisions at all political levels. We suggest that tropical LULC change models have an equally high potential to influence public opinion and impact the development of land-use policies based on plausible future scenarios, but, to do that reliably may require further improvements in the discipline. [ABSTRACT FROM AUTHOR]

The hydrology of scarce-data areas, such as the mountainous area of the Andes, is poorly known mainly owing to the lack of data. Global gridded climatological datasets (GGCDs) are becoming more precise and common, but the utility of these datasets and their applicability to complex hydrological systems are still not yet well determined. In this paper the reliability of a GGCD is evaluated as an alternative source to supply the lacking in situ observations, with the aim of studying the hydrology of a mountainous area in south-central Chile. The GGCDs are found to recover the climate variability in Andean areas, and partially recover the orographic effect caused by the mountains. A finding of practical relevance is that GGCDs could be considered as an adequate source for performing hydrological models and studies under high spatial variability and scarce-data environments such as the Andes. [ABSTRACT FROM AUTHOR]

A fast, robust and scalable methodology to examine, quantify, and visualize climate patterns and their relationships is proposed. It is based on a set of notions, algorithms and metrics used in the study of graphs, referred to as complex network analysis. The goals of this approach are to explain known climate phenomena in terms of an underlying network structure and to uncover regional and global linkages in the climate system, while comparing general circulation models outputs with observations. The proposed method is based on a two-layer network representation. At the first layer, gridded climate data are used to identify 'areas', i.e., geographical regions that are highly homogeneous in terms of the given climate variable. At the second layer, the identified areas are interconnected with links of varying strength, forming a global climate network. This paper describes the climate network inference and related network metrics, and compares network properties for different sea surface temperature reanalyses and precipitation data sets, and for a small sample of CMIP5 outputs. [ABSTRACT FROM AUTHOR]

Understanding the seasonal behaviour of a subtropical forest and its inter-annual variation is crucial to understanding and monitoring its ecosystem function in the context of global warming. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index dataset, a wavelet transform method was used to investigate the inter-annual variations of vegetation phenology in a subtropical mountain and hill region in Fujian, China, during 2001-2010. The results show a distinct inter-annual variation of vegetation phenology related to climate variability even if most areas presented non-significant trends. The start dates significantly advanced and end dates delayed in 2003 and 2008, due to anomalously warm conditions. There was generally a gradient of increasing start dates, and earlier end dates of vegetation growing season, due to colder temperatures at higher altitudes. However, the altitudinal phenology relationship also depends on its corresponding rainfall conditions. Earlier start dates were observed at higher altitudes during rainfall deficit years such as 2008, which coincides with relatively abundant rainfall at higher altitudes. This paper reveals that vegetation phenology was coupled with altitudinal gradient, with distinct responses at different combinations of alternate temperature and precipitation conditions variability. [ABSTRACT FROM AUTHOR]

Natural populations experience environmental conditions that vary across space and over time. This variation is often correlated between localities depending on the geographical separation between them, and different species can respond to local environmental fluctuations similarly or differently, depending on their adaptation. How this emerging structure in environmental correlation (between-patches and between-species) affects spatial community dynamics is an open question. This paper aims at a general understanding of the interactions between the environmental correlation structure and population dynamics in spatial networks of local communities (metacommunities), by studying simple two-patch, two-species systems. Three different pairs of interspecific interactions are considered: competition, consumer–resource interaction, and host–parasitoid interaction. While the results paint a relatively complex picture of the effect of environmental correlation, the interaction between environmental forcing, dispersal, and local interactions can be understood via two mechanisms. While increasing between-patch environmental correlation couples immigration and local densities (destabilising effect), the coupling between local populations under increased between-species environmental correlation can either amplify or dampen population fluctuations, depending on the patterns in density dependence. This work provides a unifying framework for modelling stochastic metacommunities, and forms a foundation for a better understanding of population responses to environmental fluctuations in natural systems. [ABSTRACT FROM AUTHOR]