Latest Climate Projections Research Articles

Impact of climate change and genetic development on Iowa corn yield

The vulnerability of corn yield to high temperature and insufficient rainfall in the US mid-west is widely acknowledged. The impact of extreme weather and genetic development on corn yield is less well known. One of the main reasons is that the multicollinearity in the variables can lead to confounding results. Here we model the impact of climate and genetic development by employing an elastic net regression model to address the multicollinearity issue. This allows us to develop a more robust multiple regression model with higher predictive accuracy. Using granular data for Iowa from 1981-2018, we find that corn yield is vulnerable to high mean summer temperatures particularly in July, a widening diurnal temperature range in June and dry summer conditions (due to extremely low rainfall) from June-August. We find that overall climate impact reduced average annual yield by 0.7%. We also find that genetic development which led to earlier planting dates, widening duration of the reproductive interval, higher growing degree day accumulation and larger net planted area had a beneficial impact on the Iowa corn yield during 1981-2018 resulting in an average annual yield improvement of 1.8% per annum. This provides a basis for optimism that these genetic developments and management practices will continue to adapt and improve in the future to counter the impact of climate change on corn yield. We have also modelled the impact of future climate change using the latest climate projections from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). These climate projections show that the average temperature during the growing season (MayO-October) will increase by 2.4 -2.9 o C by mid-century while the average spring temperature (March and April) will increase by a relatively slower 1.9 -2.3 o C by mid-century. Additionally, climate projections show that both temperature and rainfall will also become more extreme in the future with the changes varying from spring to summer. Our results show that, just due to climate change alone in Iowa corn yield will decline between 1.4-1.7% per annum until mid-century (or 1.2-2.1% per annum until the late twenty first century).

Univariate and bivariate spatiotemporal characteristics of heat waves and relative influence of large-scale climate oscillations over India

Heat waves (HWs) have grown in frequency, duration, and intensity in last few decades globally and even the latest climate projections (CMIP6) reveal these to get worsen under enhanced global warming. HWs have adverse impacts over biophysical and human systems, therefore, to analyse the impacts of HWs accurately, it is required to understand the regional trend and variability associated with HWs. We carried out a bivariate copula-based joint exceedance probabilistic analysis of HW characteristics to better assess the vulnerability of Indian regions. The regions with comparatively higher HW frequency, higher intensity, longer duration, and lower joint return period of HW frequency and intensity were selected as vulnerable to HWs. Rajasthan, Madhya Pradesh, Uttar Pradesh, Punjab, Bihar, Chhattisgarh, Gujrat, Karnataka, Maharashtra, Telangana, Andhra Pradesh, and Odisha states of India have been found vulnerable and are projected to get even more vulnerable in the future as per the recent climate projections. To unveil the spatiotemporal variability in HW characteristics, we investigated the relative influence of crucial large-scale climate oscillations (LSCOs) i.e., ENSO, IOD, and AMO on HW by introducing these atmospheric variables as covariates in nonstationary extreme value theory (EVT)-based models. The statistical analysis from generalized extreme value distribution of temperature extreme with LSCOs reveals that ENSO and IOD simultaneously have been influencing the extreme in 21.2%, 25.9% and 23.1% of total region taken into consideration in April, May and June month respectively.

Integrated impact assessment of climate change and hydropower operation on streamflow and inundation in the lower Mekong Basin

Water resources are key to economic development of the Mekong River Basin, but are threatened by climate change and affected by hydropower development. Knowledge of these drivers’ integrated impact on future hydrological alterations is limited, especially with respect to flood inundation in the lower basin. This study assesses streamflow and flood extent alterations by reservoir operations and climate change using the latest climate projections. A distributed hydrologic model is used to generate discharge and flood extent. Our findings indicate substantial changes in seasonal and annual peak discharge due to reservoir operations. Under the future hydropower scenario, the discharge at Kratie will change by + 28% ( − 10%) during the dry (wet) season. While the effects of hydropower operations vary by season, climate change tends to increase river discharge overall. Under the high-emission scenario, the wet seasonal flow at Kratie will increase by + 7% in the near-future (2026–2050), but change by -5% under integrated impact of climate change and reservoir operations. In the far-future, the wet seasonal flow at Kratie under climate change only (integrated impact) will increase by + 33% (+ 19%). Although climate change is the dominant driver of flow alterations, hydropower development is critical for reducing discharge and flood magnitude. Nonparametric statistical testing shows significant changes in the inundated area by up to + 37% during the projected periods.

Impacts of 1.5 °C and 2.0 °C global warming above pre-industrial on potential winter wheat production of China

Abstract Keeping global temperatures below 2.0 °C above pre-industrial condition and pursuing efforts toward the more ambitious 1.5 °C goal in the late 21st century was the main target from the Paris Agreement in 2015. Here we assessed the likely challenges for the China’s winter wheat production under 1.5 °C and 2.0 °C increase of global temperature, with four wheat crop models (CERES-Wheat, Nwheat, WheatGrow, and APSIM-Wheat) and the latest climate projections from the Half a degree Additional warming, Prognosis and Projected Impacts project (HAPPI). Instead of using average “winter type” wheat cultivar, and same management and soil inputs for whole region, location-specific winter wheat cultivars with local agronomic information were calibrated for each of the representative wheat growing area of China, allowing a better spatial agronomic representation of the whole wheat planting area. The mean growing season temperature (GST) during the winter wheat vegetative stage was projected to increase by 0.6 to 1.4 °C for the 1.5 °C scenario, and 0.9 to 1.8 °C for the 2.0 °C scenario, while during the reproductive stage was decreased between 0 and 0.9 °C for the 1.5 °C scenario and -0.3 and 1.1 °C for the 2.0 °C scenario. Growing season duration (GSD) for the whole period was shortened by 6 to 15 days for the 1.5 °C scenario and 8 to 18 days for the 2.0 °C scenario, as a result of higher GST under global warming. Increase in GST and decrease in GSD was more obvious in the Southwest Subregion (SWS) than subregions in the north. The shortening GSD for the whole wheat growth period was mostly from the shortening vegetative period, as no appreciable difference in number of days from anthesis to maturity was found for the whole regions. Although there is variability among models, the indication is that wheat yields were projected to increase in the North Subregion (NS), the Huang-Huai Subregion (HHS), and the Middle-lower Researches of Yangzi River Subregion (MYS), but to decrease in the SWS under two warming scenarios. The effects of elevated CO2 concentration were mostly beneficial and tended to offset the negative impacts of increasing temperature at both global warming scenarios, with a rate of 7-14% yield increase per 100-ppm, except for locations with GST of baseline higher than 11 °C. Aggregating to regional wheat production, the total winter wheat production of China was projected to increase by 2.8% (1.6% to 3.0%, 25th percentile to 75th percentile) and 8.3% (7.0% to 9.6%, 25th percentile to 75th percentile) under 1.5 °C and 2.0 °C scenarios, and most of increase was observed in the north subregions due to the largest wheat planting area. Our results will lay the foundation for developing adaptation strategies to future climate change to ensure China and global wheat supply and food security.

Towards Adaptation of Water Resource Systems to Climatic and Socio-Economic Change

Climate change is viewed as the major threat to the security of water supplies in most parts of the world in the coming decades, and the water resources literature continues to be dominated by impact and risk assessments based on the latest climate projections from General Circulation Models (GCMs). However, the evidence for anthropogenic changes in precipitation and streamflow records continues to be elusive which, together with the known high uncertainty in GCM ensemble projections, has led to the development of risk assessment methods which are not driven exclusively by GCMs. It is argued that a baseline risk assessment should retain the assumption of climatic stationarity, and be based on the modelling of observed interannual variability as a dominant process in determining water resource system reliability, augmented where justifiable by reliable information from GCMs. However, irrespective of what the climate does in the future, globalization and socio-economic changes are the major drivers for increases in water demand and threats to water security, as exemplified by the burgeoning economies of the BRIC and MINT countries, and the large population increases and economic growth seen in many developing countries. It is suggested that more attention needs to be paid to adaptation to socio-economic change which is arguably more predictable than climatic change, based on what is already known about population and economic growth, lifestyle changes and human choices. More focus is needed on economic analyses that can inform the major investments in water use efficiency measures which can deliver the water savings needed to avert widespread water scarcity. The effectiveness of water use efficiency measures is largely determined by (a) the potential of modern information technology to achieve more efficient water resources management and water use and (b) human responses and choices in the uptake of measures. To assess the potential efficiency gains, it is argued that water resource systems modelling needs to evolve to incorporate the human dimension more explicitly, through Coupled Human and Natural Systems (CHANS) modelling. A CHANS modelling framework is outlined which incorporates agent-based modelling to represent individual choices within the human system, and prospects for assessing the effectiveness of efficiency measures involving individual human responses are discussed.

Tailoring a future overheating risk tool for existing building design practice in domestic and non-domestic sectors

The Low Carbon Futures Project, as part of the Adaptation and Resilience to Climate Change (ARCC) programme, has developed an overheating tool, based on probabilistic UK Climate Projections (UKCP09), to provide design advice for building adaptations in future. For dwellings this tool, initiated by a single simulation, relies on just hourly climate information to predict the internal temperature profile and for a non-domestic building, it includes internal activity profiles to account for lighting, equipment, metabolic gains and air change. To produce a tailored design tool, a qualitative investigation has been carried out to understand current building practices. This investigation shows that the two sectors take a significantly different approach to design, where dynamic building simulation is rare for domestic developments. The diversity of the non-domestic building stock poses different challenges and requires more detail to perform any overheating analysis, with dynamic building simulation playing a key role. The suitability of this tool, and the need to balance complexity and detail with usability and applicability, will be explored for the two sectors, with an approach for implementing this in the future proposed. Practical application: This paper compares current overheating analyses, as reported through correspondence with practitioners, with a suggested approach for a more detailed future overheating assessment using the latest climate projections. The required steps to bridge the gap between current and possible future design methods are explored for both the domestic and non-domestic sectors, with a prototype tool proposed that has been formulated with the aid of industry feedback. The described project is therefore able to translate complex building and climate science into an approach that is potentially useful for building practitioners.

Constructing a future weather file for use in building simulation using UKCP09 projections

In the future with climate change, building designers may need to demonstrate to clients that their buildings will continue to provide a comfortable environment under future weather conditions. Building simulation modelling can be used to this end provided that suitable weather files can be constructed. This article describes the use of the latest climate projections from UKCIP (UKCP09 data) and a method of constructing an hourly weather file for the following parameters: dry-bulb temperature, relative humidity, cloud cover, solar irradiation (direct and diffuse), wind speed and wind direction. For a given future scenario, the approach used first selects appropriate months from 3000 years of UKCP09 data to construct a Test Reference Year (TRY). Unfortunately, the UKCP09 hourly data has no wind or cloud data, and the solar irradiance is uncorrected at low sun angles. This article describes algorithms for calculating wind speed and cloud cover from the UKCP09 data. From this data, a TRY can be constructed and formatted to be suitable for use with commonly used simulation packages. Practical applications: Building professionals increasingly seek reassurance on how a proposed building will perform under a future rather than historical climate. This article describes a method of processing the latest future climate projections (UKCP09 data released in June 2009) and generating a Test Reference Year (TRY) with the full complement of weather parameters suitable for use by commonly used building simulation programmes.