Budong Qian

Impacts of land use/land cover change on climate and future research priorities.

Several recommendations have been proposed for detecting land use and land cover change (LULCC) on the environment from, observed climatic records and to modeling to improve its understanding and its impacts on climate. Researchers need to detect LULCCs accurately at appropriate scales within a specified time period to better understand their impacts on climate and provide improved estimates of future climate. The US Climate Reference Network (USCRN) can be helpful in monitoring impacts of LULCC on near-surface atmospheric conditions, including temperature. The USCRN measures temperature, precipitation, solar radiation, and ground or skin temperature. It is recommended that the National Climatic Data Center (NCDC) and other climate monitoring agencies develop plans and seek funds to address any monitoring biases that are identified and for which detailed analyses have not been completed.

Observed Long-Term Trends for Agroclimatic Conditions in Canada

A set of agroclimatic indices representing Canadian climatic conditions for field crop production are analyzed for long-term trends during 1895–2007. The indices are categorized for three crop types: cool season, warm season, and overwintering. Results indicate a significant lengthening of the growing season due to a significantly earlier start and a significantly later end of the growing season. Significant positive trends are also observed for effective growing degree-days and crop heat units at most locations across the country. The occurrence of extremely low temperatures has become less frequent during the nongrowing season, implying a more favorable climate for overwinter survival. In addition, the total numbers of cool days, frost days, and killing-frost days within a growing season have a decreasing trend. This means that crops may also be less vulnerable to cold stress and injury during the growing season. Extreme daily precipitation amounts and 10-day precipitation totals during the growing season have been increasing. Significant trends associated with increased availability of water during the growing season are identified by the standardized precipitation index and seasonal water deficits. The benefit of the increased precipitation may have been offset by an upward trend in evaporative demand; however, this would depend on the amount of growth and productivity resulting from increased actual evapotranspiration.

Changing growing season observed in Canada

It is theoretically interesting for climate change detection and practically important for agricultural producers to know whether climate change has influenced agroclimatic conditions and, if so, what the potential impacts are. We present analyses on statistical differences in means and variances of agroclimatic indices between three 30-year periods in the 20th century (i.e., 1911–1940, 1941–1970 and 1971–2000). We found many occurrences of statistically significant changes in means between pairs of the three 30-year periods. The findings consistently support agroclimatic trends identified from trend analysis as an earlier growing season start and an earlier end to spring frost (SF), together with an extended growing season, more frost-free days (FFD) and more available heat units were often found in the later 30-year periods as compared to the earlier ones. In addition, this study provides more detailed quantitative information than the trend signals for the practical interests of agricultural applications. Significant changes were detected for SF and FFD at a much larger percentage of stations between the latter two 30-year periods (1941–1970 vs. 1971–2000) as compared to the earlier two periods (1911–1940 vs. 1941–1970). In contrast, changes in variances of the selected agroclimatic indices were less evident than changes in their means, based on the percentage of stations showing significant differences. We also present new climate averages of the selected agroclimatic indices that can be useful for agricultural planning and management.

Adjusted Daily Rainfall and Snowfall Data for Canada

ABSTRACT This article documents how Environment and Climate Change Canada’s Adjusted Daily Rainfall and Snowfall (AdjDlyRS) dataset was developed. The adjustments include (i) conversion of ruler measurements of snowfall to its water equivalent using a previously developed snow water equivalent (SWE) ratio map for Canada; (ii) corrections for gauge-related issues including undercatch and evaporation caused by wind effects and gauge-specific wetting loss, as well as for trace precipitation amounts, using previously developed procedures for Canada. Various data flags (e.g., accumulation flags) were also treated. This dataset contains all Canadian stations reporting daily rainfall and snowfall for which we have metadata to implement the adjustments. The length of the data record varies from one station to another, starting as early as 1840. The results show that the original unadjusted total precipitation data in Environment and Climate Change Canada’s digital archive underestimate the total precipitation in northeastern Canada by more than 25% and by about 10–15% in most of southern Canada. Such large underestimates make the original data unsuitable for water availability and/or balance studies or for numerical model validation, among many other applications. The use of the assumed 10:1 SWE ratio for the archived total precipitation data is the primary cause of the underestimate, which is most severe in northeastern Canada. The trace correction adds 5–20% to precipitation values in northern Canada but less than 5% in southern Canada. The gauge-related corrections do not show an organized spatial pattern but add 5–10% to the precipitation at 312 stations. Long runs (≥3 months) of miscoded missing values were also identified and corrected. The latest version of the AdjDlyRS dataset is available from the Canadian Open Data Portal; currently it is version 2016, which contains 3346 stations and covers the period from station inception to February 2016. This dataset is suitable for producing gridded precipitation datasets, as well as other applications.

Indices of Canada’s future climate for general and agricultural adaptation applications

This study evaluates regional-scale projections of climate indices that are relevant to climate change impacts in Canada. We consider indices of relevance to different sectors including those that describe heat conditions for different crop types, temperature threshold exceedances relevant for human beings and ecological ecosystems such as the number of days temperatures are above certain thresholds, utility relevant indices that indicate levels of energy demand for cooling or heating, and indices that represent precipitation conditions. Results are based on an ensemble of high-resolution statistically downscaled climate change projections from 24 global climate models (GCMs) under the RCP2.6, RCP4.5, and RCP8.5 emissions scenarios. The statistical downscaling approach includes a bias-correction procedure, resulting in more realistic indices than those computed from the original GCM data. We find that the level of projected changes in the indices scales well with the projected increase in the global mean temperature and is insensitive to the emission scenarios. At the global warming level about 2.1xa0°C above pre-industrial (corresponding to the multi-model ensemble mean for 2031–2050 under the RCP8.5 scenario), there is almost complete model agreement on the sign of projected changes in temperature indices for every region in Canada. This includes projected increases in extreme high temperatures and cooling demand, growing season length, and decrease in heating demand. Models project much larger changes in temperature indices at the higher 4.5xa0°C global warming level (corresponding to 2081–2100 under the RCP8.5 scenario). Models also project an increase in total precipitation, in the frequency and intensity of precipitation, and in extreme precipitation. Uncertainty is high in precipitation projections, with the result that models do not fully agree on the sign of changes in most regions even at the 4.5xa0°C global warming level.

ENSO and Sea Surface Temperature Anomalies in Association with Canadian Wheat Yield Variability

ABSTRACT Interannual variations of spring wheat yields in Canadian agricultural regions are analyzed, together with the associated sea surface temperature (SST) anomalies in the northern hemisphere tropics and extratropics, from 1961 to 2015. The cubic trend is calculated and used to represent the trend related to advances in agricultural technology over this time period. The correlations between Canadian wheat yields at regional scales and the tropical El Niño–Southern Oscillation (ENSO) variability are not robust at any stage of the evolution of ENSO. Based on the power spectrum and cross-spectrum analysis, the most prominent yield variance is found in the Canadian Prairies, with a significant power peak of 4.5 years but does not co-vary significantly with interannual ENSO variability. ENSO weakly affects temperature and precipitation anomalies in the Canadian Prairie Region in summer—two important agroclimatic conditions for crop growth—and hence insignificantly impacts wheat yields. This indicates that there would be little benefit to including tropical ENSO indices in the operational wheat yield forecasting system. For Canadian wheat yield forecasting, attention should be paid to the preceding winter and spring SST anomalies in the northern extratropics. The SST anomalies associated with yields in the Canadian Prairie region and Central Region are generally stronger than those associated with yields in the Canadian Pacific Coast Region and eastern Maritime Region. In association with the Prairie Region and Central Region yields, SST shows pronounced anomalies in the mid-high latitudes of the North Pacific from winter to summer. The non-linearity of the SST anomalies associated with the Canadian yields is also clearly evident. Stronger (weaker) SST anomalies in the extratropical North Pacific correspond to low wheat yields in the Prairie (Central) Region, while weaker (stronger) SST anomalies correspond to high yields in the Prairie (Central) Region.