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Using an ensemble of 10 statistically downscaled global climate model (GCM) simulations, we project future climate change impacts on the state of Indiana (IN) for two scenarios of greenhouse gas concentrations (a medium scenario—RCP4.5 and a high scenario—RCP 8.5) for three future time periods (2020s, 2050s, 2080s). Relative to a 1971–2000 baseline, the projections show substantial changes in temperature (T) for IN, with a change in the annual ensemble mean T for the 2080s RCP8.5 scenario of about 5.6 °C (10.1 °F). Such changes also indicate major changes in T extremes. For southern IN, the number of days with daily maximum T above 35 °C (95 °F) is projected to be about 100 days per year for the 2080s RCP8.5 scenario, as opposed to an average of 5 days for the historical baseline climate. Locations in northern IN could experience 50 days per year above 35 °C (95 °F) for the same conditions. Energy demand for cooling, as measured by cooling degree days (CDD), is projected to increase nearly fourfold in response to this extreme warming, but heating demand as measured by heating degree days (HDD) is projected to decline by 30%, which would result in a net reduction in annual heating/cooling energy demand for consumers. The length of the growing season is projected to increase by about 30 to 50 days by the 2080s for the RCP8.5 scenario, and USDA hardiness zones are projected to shift by about one full zone throughout IN. By the 2080s, all GCM simulations for the RCP8.5 scenario show higher annual precipitation (P) over the Midwest and IN. Projected seasonal changes in P include a 25–30% increase in winter and spring by the 2080s for the RCP8.5 scenarios and a 1–7% decline in summer and fall P (although there is a low model agreement in the latter two seasons). Rising T is projected to cause systematic decreases in the snow-to-rain ratio from Nov-Mar. Snow is projected to become uncommon in southern IN by the 2080s for the RCP8.5 scenario, and snowfall is substantially reduced in other areas of the state. The combined effects of these changes in T, P, and snowfall will likely result in increased surface runoff and flooding during winter and spring.

Tree rings are widely considered to be a reliable proxy record of variations in climate and soil moisture. Here, using data from the Midwest United States (US), we provide documentation of a deteriorating relationship between radial tree growth and drought that is consistent across multiple species and locations. We find that traditional methods for drought reconstructions produce models that have rapidly declining validation statistics in recent decades. Split-sample calibration-verification that uses the first and second halves of the record can be problematic, as those two samples may not represent a sufficiently wide range of soil moisture conditions. To investigate this problem, we develop a randomized validation procedure that generates an empirical distribution of calibration and validation statistics. We place validation statistics derived from traditional methods in the generated distribution and compare them to a stratified approach that ensures each calibration model is composed of a sample that includes both dry and wet years. We find that the deteriorating relationship between tree growth and soil moisture is an artifact of the absence of drought over an extended period of time. A model that forces each calibration period to contain extreme drought years is statistically validated.. Nonetheless, if the current pluvial continues in the Midwest US, the linear relationship between tree rings and soil moisture will likely continue to deteriorate to the point where tree rings in the region will have a reduced ability to estimate past drought conditions.

Using daily minimum air-temperature (Tmin) data from the state of Illinois, the dates of spring and fall freezes – and the resulting growing-season length – are examined for trends during theperiod 1906–1997. Of the stations in the Daily Historical Climate Network, mostshow trends toward earlier spring freezes; however, trends in fall freezes are not consistent over the station network. Although the time series are highly variable (noisy), results suggest that the growing-season length in Illinois became roughly one week longer during the 20thcentury. To examine how changing freeze-date statistics relate to changing air-temperature probability distributions, percentiles of Tmin formoving 10-year periods were analyzed for trends during the typical times for spring and fall freezes in Illinois (i.e., the months of April and October). The lower portion of the April probability distribution shows substantially larger warming (0.5–0.7 ° C/100 yrs) than the upper portion of the distribution (0.2–0.3 ° C/100 yrs), suggesting that although cold events are warming during April, warm events are not warming as fast. Conversely, the lower portion of the October probability distribution shows modest cooling in Tmin (–0.2 ° C/100yrs for the 10th percentile), while middle and upper portions of the distribution show very large rates of cooling (up to –1.5 ° C/100 yrs for the 40th–70th percentiles). Analysis ofthe entire probability distribution provides a more-comprehensive perspective on climatic change than does the traditional focus on central tendency.

Uneven and changing spatial distributions of air temperature stations can produce unrepresentative samples of the space-time variability of near-surface air temperature. Over the last century, station networks have varied from less than 300 stations that are largely limited to the Northern Hemisphere to nearly 1700 stations that are fairly well distributed over the terrestrial surface. As a result, estimates of air temperature change derived from historical observation networks often contain network-induced variability. Spatial resampling methods are used to estimate network-induced variability in terrestrially averaged air temperature anomalies and trends. Random resampling from the station networks allows pseudo confidence intervals and other statistics of variability to be estimated. Network-induced variability appears to be substantial during the late 1800s, especially in the Southern Hemisphere. Most networks from the 1900s and the Northern Hemisphere, however, appear to produce reliable estimates of spatially averaged air temperature anomalies and trends. A non-random, but historically appropriate, resampling method - sampling a given year's air temperature anomaly field using another year's station distribution - also is used. Using 1987 - one of the warmest years on record - as an example, station distributions from 1881-1988 are used to sample the 1987 air temperature anomaly field. This sampling procedure produces spatially averaged air temperature anomalies for 1987 that vary by over 0.3°C. A combinatorial process of resampling a given year's anomaly field is repeated for every year and every station network to produce terrestrial average air temperature anomaly estimates that vary by more than 0.3°C solely due to network changes.