Dynamics of water-energy-food nexus interactions with climate change and policy options

Understanding the dynamics of water-energy-food (WEF) nexus interactions with climate change and human intervention helps inform policymaking. This study demonstrates the WEF nexus behavior under ensembles of climate change, transboundary inflows, and policy options, and evaluates the overall nexus performance using a previously developed system dynamics-based WEF nexus model—WEF-Sask. The climate scenarios include a baseline (1986-2014) and near-future climate projections (2021-2050). The approach is demonstrated through the case study of Saskatchewan, Canada. Results show that rising temperature with increased rainfall likely maintains reliable food and feed production. The climate scenarios characterized by a combination of moderate temperature increase and slightly less rainfall or higher temperature increase with slightly higher rainfall are easier to adapt to by irrigation expansion. However, such expansion uses a large amount of water resulting in reduced hydropower production. In contrast, higher temperature, combined with less rainfall, such as SSP370 (2.4 ℃, -6 mm), is difficult to adapt to by irrigation expansion. Renewable energy expansion, the most effective climate change mitigation option in Saskatchewan, leads to the best nexus performance during 2021-2050, reducing total water demand, groundwater demand, greenhouse gas (GHG) emissions, and potentially increasing water available for food production. In this study, we recommend and use food and power production targets and provide an approach to assessing the impacts of hydroclimate and policy options on the WEF nexus, along with suggestions for adapting the agriculture and energy sectors to climate change.

Does Model Calibration Reduce Uncertainty in Climate Projections?

Uncertainty in climate projections is large as shown by the likely uncertainty ranges in Equilibrium Climate Sensitivity (ECS) of 2.5-4K and in the Transient Climate Response (TCR) of 1.4-2.2K. Uncertainty in model projections could arise from the way in which unresolved processes are represented, the parameter values used, or the targets for model calibration. We show that, in two climate model ensembles which were objectively calibrated to minimise differences from observed large scale atmospheric climatology, uncertainties in ECS and TCR are about two to six times smaller than in the CMIP5 or CMIP6 multi-model ensemble. We also find that projected uncertainties in surface temperature, precipitation and annual extremes are relatively small. Residual uncertainty largely arises from unconstrained sea-ice feedbacks. The 20+ year old HadAM3 standard model configuration simulates observed hemispheric scale observations and pre-industrial surface temperatures about as well as the median CMIP5 and CMIP6 ensembles while the optimised configurations simulates these better than almost all the CMIP5 and CMIP6 models. Hemispheric scale observations and pre-industrial temperatures are not systematically better simulated in CMIP6 than in CMIP5 though the CMIP6 ensemble seems to better simulate patterns of large-scale observations than the CMIP5 ensemble and the optimised HadAM3 configurations. Our results suggest that most CMIP models could be improved in their simulation of large scale observations by systematic calibration. However, the uncertainty in climate projections (for a given scenario) likely largely arises from the choice of parametrisation schemes for unresolved processes (“structural uncertainty”), with different tuning targets another possible contributor.

A Robust Arctic Amplification Factor Throughout the Last 21,000 Years

Arctic amplification (AA), a phenomenon that a larger change in temperature near the Arctic areas than the Northern Hemisphere average in the past 100+ years, has significant impacts on mid-latitude weather and climate, and therefore is of great concern in current climate projections. Previous studies suggest a wide range of AA factors from 1.0 to 12.5 using either the 20th century observations or climate model hindcasts. In the present paper, we explore the diversity of AA factor in a long-term transient simulation covering the past glacial-to-interglacial years. It is shown that the natural AA phenomenon is essentially linked with North Atlantic sea ice changes through ice-albedo feedback with a narrowed and robust AA factor of 2.5±0.8 throughout the last 21,000 years. Current observed AA phenomenon is a mixed result combining sea ice melting induced AA mode with GHGs induced global uniform warming, and thus has an AA factor slightly less than 2.5. In the future, as Arctic sea ice gradually melts off, we speculate that AA phenomenon might fade off accordingly and the AA factor will decline close to 1.0 in 1-2 centuries. Our findings provide new evidence for better understanding the range of AA factor and associated key physical processes, and provide new insights for AA’s projection in current anthropogenic warming climate.

WRF Sensitivity for Seasonal Climate Simulations of Precipitation Fields on the CORDEX South America Domain

Dynamic numerical models of the atmosphere are the main tools used for weather and climate forecasting as well as climate projections. Thus, this work evaluated the systematic errors and areas with large uncertainties in precipitation over the South American continent (SAC) based on regional climate simulations with the weather research and forecasting (WRF) model. Ten simulations using different convective, radiation, and microphysical schemes, and an ensemble mean among them, were performed with a resolution of 50 km, covering the CORDEX-South America domain. First, the seasonal precipitation variability and its differences were discussed. Then, its annual cycle was investigated through nine sub-domains on the SAC (AMZN, AMZS, NEBN, NEBS, SE, SURU, CHAC, PEQU, and TOTL). The Taylor Diagrams were used to assess the sensitivity of the model to different parameterizations and its ability to reproduce the simulated precipitation patterns. The results showed that the WRF simulations were better than the ERA-interim (ERAI) reanalysis when compared to the TRMM, showing the added value of dynamic downscaling. For all sub-domains the best result was obtained with the ensemble compared to the satellite TRMM. The largest errors were observed in the SURU and CHAC regions, and with the greatest dispersion of members during the rainy season. On the other hand, the best results were found in the AMZS, NEBS, and TOTL regions.

Different mechanisms of Arctic and Antarctic sea ice response to ocean heat transport

Understanding drivers of Arctic and Antarctic sea ice on multidecadal timescales is key to reducing uncertainties in long-term climate projections. Here we investigate the impact of ocean heat transport (OHT) on sea ice, using pre-industrial control simulations of 20 models participating in the latest Coupled Model Intercomparison Project (CMIP6). In all models and in both hemispheres, sea ice extent is negatively correlated with poleward OHT. However, the similarity of the correlations in both hemispheres hides radically different underlying mechanisms. In the northern hemisphere, positive OHT anomalies primarily result in increased ocean heat convergence along the Atlantic sea ice edge, where most of the ice loss occurs. Such strong, localised heat fluxes ($$\sim {}100~\text {W}~\text {m}^{-2}$$ ∼ 100 W m - 2 ) also drive increased atmospheric moist-static energy convergence at higher latitudes, resulting in a pan-Arctic reduction in sea ice thickness. In the southern hemisphere, increased OHT is released relatively uniformly under the Antarctic ice pack, so that associated sea ice loss is driven by basal melt with no direct atmospheric role. These results are qualitatively robust across models and strengthen the case for a substantial contribution of ocean forcing to sea ice uncertainty, and biases relative to observations, in climate models.

Observed and Projected Scaling of Daily Extreme Precipitation with Dew Point Temperature at Annual and Seasonal Scales across the Northeast United Sates

This study investigates how extreme precipitation scales with dew point temperature across the Northeast U.S., both in the observational record (1948-2020) and in a set of downscaled climate projections in the state of Massachusetts (2006-2099). Spatiotemporal relationships between dew point temperature and extreme precipitation are assessed, and extreme precipitation – temperature scaling rates are evaluated on annual and seasonal scales using non-stationary extreme value analysis for annual maxima and partial duration series, respectively. A hierarchical Bayesian model is then developed to partially pool data across sites and estimate regional scaling rates, with uncertainty. Based on the observations, the estimated annual scaling rate is 5.5% per °C, but this varies by season, with most non-zero scaling rates in summer and fall and the largest rates (∼7.3% per °C) in the summer. Dew point temperatures and extreme precipitation also exhibit the most consistent regional relationships in the summer and fall. Downscaled climate projections exhibited different scaling rates compared to the observations, ranging between -2.5 and 6.2% per °C at an annual scale. These scaling rates are related to the consistency between trends in projected precipitation and dew point temperature over the 21st century. At the seasonal scale, climate models project larger scaling rates for the winter compared to the observations (1.6% per °C). Overall, the observations suggest that extreme daily precipitation in the Northeast U.S. only thermodynamic scales with dew point temperature in the warm season, but climate projections indicate some degree of scaling is possible in the cold season under warming.

Climate Projections Over Different Climatic Regions of Afghanistan for Shared Socioeconomic Scenarios

The global climate models (GCMs) of Coupled Model Intercomparison Project phase 6 (CMIP6) were used spatiotemporal projections of precipitation and temperature over Afghanistan for three shared socioeconomic pathways (SSP1-2.6, 2-4.5 and 5-8.5) and two future time horizons, early (2020-2059) and late (2060-2099). The Compromise Programming (CP) approach was employed to order the GCMs based on their skill to replicate precipitation and temperature climatology for the reference period (1975-2014). Three models, namely ACCESS-CM2, MPI-ESM1-2-LR, and FIO-ESM-2-0, showed the highest skill in simulating all three variables, and therefore, were chosen for the future projections. The ensemble mean of the GCMs showed an increase in maximum temperature by 1.5-2.5oC, 2.7-4.3 oC, and 4.5-5.3 oC and minimum temperature by 1.3-1.8 oC, 2.2-3.5 oC, and 4.6-5.2 oC for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively in the later period. Meanwhile, the changes in precipitation in the range of -15-18%, -36-47% and -40-68% for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The temperature and precipitation were projected to increase in the highlands and decrease over the deserts, indicating dry regions would be drier and wet regions wetter.

Assessing the Impacts of Climate Change on Surface Water Resources Using WEAP Model in Narok County, Kenya

Narok County in Kenya is the home to the Maasai Mara Game Reserve, which offers important habitats for a great variety of wild animals, hence, a hub for tourist attraction, earning the county and country an extra income through revenue collection. The Mau Forest Complex in the north is a source of major rivers including the Mara River and a water catchment tower that supports other regions as well. Many rivers present in the region support several activities and livelihood to the people in the area. The study examined how the quantity of surface water resources varied under the different climate change scenarios, and the sensitivity of the region to a changing climate. Several datasets used in this study were collated from different sources and included hydro–meteorological data, Digital Elevation Model (DEM), and Coordinated Regional Downscaling Experiment (CORDEX) climate projections. The WEAP (Water Evaluation and Planning) model was applied using the rainfall–runoff (soil moisture method) approach to compute runoff generated with climate data as input. All the calculations were done on a monthly time step from the current year account to the last year of the scenario. Calibration of the model proceeded using the PEST tool within the WEAP interface. The goodness of fit was evaluated using the coefficient of determination (R2), percentage bias (PBIAS), and Nash–Sutcliffe efficiency (NSE) criterion. From the tests, it was clear that WEAP performed well in simulating stream flows. The coefficient of determination (R2) was greater than the threshold R2 > 0.5 in both periods, i.e., 0.83 and 0.97 for calibration and validation periods, respectively, for the monthly flows. A 25-year mean monthly average was chosen with two time slices (2006–2030 and 2031–2055), which were compared against the baseline (1981–2000). There will be a general decrease in water quantity in the region in both scenarios: −30% by 2030 and −23.45% by 2055. In comparison, RCP4.5 and Scenario3 (+2.5°C, +10% P) were higher than RCP8.5 and Scenario 2 respectively. There was also a clear indication that the region was highly sensitive to a perturbation in climate from the synthetic scenarios. A change in either rainfall or temperature (or both) could lead to an impact on the amount of surface water yields.

Sustainable Development in the Agrifood Rice Chain in Timor-Leste

In Timor-Leste, rice is a source of livehood and a staple food. However, it presents persistently low yield, quality, price, and value to consumers, which, allied with climate projections and pressure for higher quality and productivity, raised logistics costs, and subsidized imports, creates a need to identify drivers/inhibitors of sustainable development. This chapter investigates rice agri-food chain sustainable development by recording the main actors involved and understanding their perspectives. Interviews, questionnaires, observation, and focus group have been applied to understand how sustainable development can be triggered. Results show that actors are not accurately coordinated to find a future sustainable development. An alignment of activities, innovation, best practices, and cooperation are recommended towards a future sustainability plan as a starting point to agrifood rice development. Each element of this development should be measured and quantified in future research.