Behaviors of terrestrial ecosystems differ in different El Niños. We analyze terrestrial carbon cycle responses to two extreme El Niños (2015/16 and 1997/98), and find large differences. We find that global land–atmosphere carbon flux anomaly was about 2 times smaller in 2015/16 than in 1997/98 event, without the obvious lagged response. Then we illustrate the climatic and biological mechanisms of the different terrestrial carbon cycle responses in 2015/16 and 1997/98 El Niños regionally.

The Systematic Correlation Matrix Evaluation (SCoMaE) method applies statistical information to systematically select, transparent, nonredundant indicators for a comprehensive assessment of the Earth system state. We show that due to changing climate forcing, such as anthropogenic climate change, the ad hoc assessment indicators might need to be reevaluated. Within an iterative process, this method would allow us to select scientifically consistent and societally relevant assessment indicators.

We analysed the contribution of atmospheric factors to interannual off-shore sea-level variability in the Baltic Sea region. We identified a different atmospheric circulation pattern that is more closely linked to sea-level variability than the NAO. The inverse barometer effect contributes to that link in the winter and summer seasons. Freshwater flux is connected to the link in summer and net heat flux in winter.The new atmospheric-pattern-related wind forcing plays an important role in summer.

This paper analyses the potential role of atmospheric rivers in the explosive cyclone deepening. Using ERA-Interim reanalysis data for 1979–2011, we analyse the concurrence of atmospheric rivers and explosive cyclogenesis over the North Atlantic and North Pacific basins for the extended winter months (ONDJFM).

Ensuring sustainable food and water security is an urgent and complex challenge. As the world becomes increasingly globalised and interdependent, food and water management policies may have unintended consequences across regions, sectors and scales. Current decision-making tools do not capture these complexities and thus miss important dynamics. We present a modelling framework to capture regional and sectoral interdependence and cross-scale feedbacks within the global food system.

We present an analysis of three crops in West Africa and their response to short-term climate change in a world where temperatures are 1.5 °C above the preindustrial levels. We show that the number of crop failures for all crops is due to increase in the future climate. We further show the difference in yield change across several West African countries and show that the yields are not expected to increase fast enough to prevent food shortages.

Users presented with large multi-model ensembles commonly use the equally weighted model mean as a best estimate, ignoring the issue of near replication of some climate models. We present an efficient and flexible tool that finds a subset of models with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments.

We use global observations of current terrestrial net primary productivity (NPP) to constrain the uncertainty in large ensemble 21st century projections of NPP under a "business as usual" scenario using a skill-based multi-model averaging technique. Our results show that this procedure helps greatly reduce the uncertainty in global projections of NPP. We also identify regions where uncertainties in models and observations remain too large to confidently conclude a sign of the change of NPP.

We present here a newly implemented water vapor tracer tool into the WRF meteorological model (WRF-WVT). A detailed validation shows high accuracy, with an error of much less than 1 % in moisture traceability. As an example application, we show that for the 2014 Great Lake-effect snowstorm, above 30 % of precipitation in the regions immediately downwind originated from lake evaporation, with contributions exceeding 50 % in the areas with highest snowfall accumulations.

The United Nations Framework Convention on Climate Change invited the scientific community to explore the impacts of a world in which anthropogenic global warming is stabilized at only 1.5 °C above preindustrial average temperatures. We present a projection of future tropical cyclone statistics for both 1.5 and 2.0 °C stabilized warming scenarios using a high-resolution global climate model. We find more frequent and intense tropical cyclones, but a reduction in weaker storms.

This study uses a global climate model to explore the extent to which sea level rise due to thermal expansion of the ocean is reversible if the atmospheric concentration of carbon dioxide (CO₂) declines. It is found that sea level continues to rise for several decades after atmospheric CO₂ starts to decline and does not return to the pre-industrial level for over thousand years after atmospheric CO₂ is restored to the pre-industrial concentration.

The impacts of climate change and human activities on oasis water requirements in Heihe River basin were quantified with the methods of partial derivative and slope in this study. The results showed that the oasis water requirement increased sharply from 10.8 × 10⁸ to 19.0 × 10⁸ m³ during 1986–2013. Human activities were the dominant driving forces. Changes in climate, land scale and structure contributed to the increase in water requirement at rates of 6.9, 58.1, and 25.3 %, respectively.

Assessing projected hydroclimatological changes is crucial, but associated with large uncertainties. We statistically assess here the response of precipitation and water availability to global temperature change, enabling us to estimate the significance of drying/wetting tendencies under anthropogenic climate change. We further show that opting for a 1.5 K warming target just slightly influences the mean response but could substantially reduce the risk of experiencing extreme changes.

This paper presents a new equation for the dispersion of salinity in alluvial estuaries based on the maximum power concept. The new equation is physically based and replaces previous empirical equations. It is very useful for application in practice because in contrast to previous methods it no longer requires a calibration parameter, turning the method into a predictive method. The paper presents successful applications in more than 23 estuaries in different parts of the world.

Atmospheric rivers (ARs) account for most of the extreme flooding events on the northwestern coast of the US. In a warmer climate, ARs in this region are projected to become more frequent and intense. We present an integrated modeling system to quantify atmospheric–hydrologic–hydraulic and economic impacts of an AR event in western Washington. Our integrated modeling tool provides communities in the region with a range of possible future physical and economic impacts associated with AR flooding.

This study is the first risk-based assessment of changes in global drought at 1.5 and 2 °C warmer worlds using CMIP5 models. By keeping the warming at 1.5 °C above the preindustrial levels instead of 2 °C, the risks of drought and the affected total, urban and rural populations would decrease at global and regional scales. While challenging for both East Africa and South Asia, the benefits of limiting warming to below 1.5 °C in terms of global drought risk and impact reduction are significant.

In a series of simulations, with models of different complexity, we analyse the role of the tropical ocean dynamics in the transmission of information when an extratropical thermal forcing is imposed. In terms of annual means we find that the tropical ocean dynamics oppose the remote extratropical signal. However, changes in the sea surface temperature seasonal cycle in the equatorial Pacific Ocean become significant only once the tropical ocean dynamics are incorporated.

The United Nations Framework Convention on Climate Change challenged the scientific community to describe the impacts of stabilizing the global temperature at its 21st Conference of Parties. A specific target of 1.5 °C above preindustrial levels had not been seriously considered by the climate modeling community prior to the Paris Agreement. This paper analyzes heat waves in simulations designed for this target. We find there are reductions in extreme temperature compared to a 2 °C target.

This study uses the cumulative distribution function transform (CDF-t) method to provide bias-corrected data over Africa using WFDEI as a reference dataset. It is shown that CDF-t is very effective in removing the biases and reducing the high inter-GCM scattering. Differences with other bias-corrected GCM data are mainly due to the differences among the reference datasets, particularly for surface downwelling shortwave radiation, which has a significant impact in terms of simulated maize yields.