Description: Thermodynamics of saline and fresh water mixing in estuaries Zhilin Zhang and Hubert H. G. Savenije Earth Syst. Dynam., 9, 241-247, https://doi.org/10.5194/esd-9-241-2018, 2018 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.

Description: Regional scaling of annual mean precipitation and water availability with global temperature change Peter Greve, Lukas Gudmundsson, and Sonia I. Seneviratne Earth Syst. Dynam., 9, 227-240, https://doi.org/10.5194/esd-9-227-2018, 2018 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.

Description: Using Network Theory and Machine Learning to predict El Niño Peter D. Nooteboom, Qing Yi Feng, Cristóbal López, Emilio Hernández-García, and Henk A. Dijkstra Earth Syst. Dynam. Discuss., https//doi.org/10.5194/esd-2018-13,2018 Manuscript under review for ESD (discussion: open, 0 comments) The prediction of the El Niño phenomenon, an increased sea surface temperature in the eastern Pacific, fascinates people for a long time. El Niño is associated with natural disasters, such as droughts and floods. Current methods can make a reliable prediction of this phenomenon up to six months ahead. However, this article presents a method which combines network theory and machine learning and predicts El Niño up to one year ahead.

Description: Irreversible ocean thermal expansion under carbon dioxide removal Dana Ehlert and Kirsten Zickfeld Earth Syst. Dynam., 9, 197-210, https://doi.org/10.5194/esd-9-197-2018, 2018 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 (CO 2 ) declines. It is found that sea level continues to rise for several decades after atmospheric CO 2 starts to decline and does not return to the pre-industrial level for over thousand years after atmospheric CO 2 is restored to the pre-industrial concentration.

Description: Changes in tropical cyclones under stabilized 1.5 and 2.0 °C global warming scenarios as simulated by the Community Atmospheric Model under the HAPPI protocols Michael F. Wehner, Kevin A. Reed, Burlen Loring, Dáithí Stone, and Harinarayan Krishnan Earth Syst. Dynam., 9, 187-195, https://doi.org/10.5194/esd-9-187-2018, 2018 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.

Description: A new moisture tagging capability in the Weather Research and Forecasting model: formulation, validation and application to the 2014 Great Lake-effect snowstorm Damián Insua-Costa and Gonzalo Miguez-Macho Earth Syst. Dynam., 9, 167-185, https://doi.org/10.5194/esd-9-167-2018, 2018 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.

Description: Quantification of the impacts of climate change and human agricultural activities on oasis water requirements in an arid region: a case study of the Heihe River basin, China Xingran Liu and Yanjun Shen Earth Syst. Dynam., 9, 211-225, https://doi.org/10.5194/esd-9-211-2018, 2018 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 × 10 8 to 19.0 × 10 8  m 3 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.

Description: The biomass burning contribution to climate-carbon cycle feedback Sandy P. Harrison, Patrick J. Bartlein, Victor Brovkin, Sander Houweling, Silvia Kloster, and I. Colin Prentice Earth Syst. Dynam. Discuss., https//doi.org/10.5194/esd-2018-11,2018 Manuscript under review for ESD (discussion: open, 0 comments) Temperature exerts strong controls on the incidence and severity of fire. Warming is thus expected to increase fire-related carbon emissions, and thereby atmospheric CO 2 . But the magnitude of this feedback is very poorly known. We use a single-box model of the land biosphere to quantify this positive feedback from satellite-based estimates of biomass burning emissions for 2000–2014 CE, and from sedimentary charcoal records for the millennium before the industrial period. We derive an estimate of the centennial-scale feedback strength of 6.5 ± 3.4 ppm CO 2 per degree of land temperature increase, based on the satellite data. However, this estimate is poorly constrained, and is largely driven by the well-documented dependence of tropical deforestation and peat fires on climate variability patterns linked to the El Niño-Southern Oscillation. Palaeodata from pre-industrial times provide the opportunity to assess the fire-related climate-carbon cycle feedback over a longer period, with less pervasive human impacts. Past biomass burning can be quantified based on variations in either the concentration and isotopic composition of methane in ice cores (with assumptions about the isotopic signatures of different methane sources) or the abundances of charcoal preserved in sediments, which reflect landscape-scale changes in burnt biomass. These two data sources are shown here to be coherent with one another. The more numerous data from sedimentary charcoal, expressed as normalized anomalies (fractional deviations from the long-term mean), are then used – together with an estimate of mean biomass burning derived from methane isotope data – to infer a feedback strength of 5.6 ± 3.2 ppm CO 2 per degree of land temperature and (for a climate sensitivity of 2.8 K) a gain of 0.09 ± 0.05. This finding indicates that the positive feedback from increased fire provides a substantial contribution to the overall climate-carbon cycle feedback on centennial time scales.

Description: Selecting a climate model subset to optimise key ensemble properties Nadja Herger, Gab Abramowitz, Reto Knutti, Oliver Angélil, Karsten Lehmann, and Benjamin M. Sanderson Earth Syst. Dynam., 9, 135-151, https://doi.org/10.5194/esd-9-135-2018, 2018 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.

Description: Reliability ensemble averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties Jean-François Exbrayat, A. Anthony Bloom, Pete Falloon, Akihiko Ito, T. Luke Smallman, and Mathew Williams Earth Syst. Dynam., 9, 153-165, https://doi.org/10.5194/esd-9-153-2018, 2018 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.