The impact of clouds, land use and snow cover on climate in the Canadian Prairies

Betts, Alan K.; Desjardins, Raymond L.; Worth, Devon E.

doi:10.5194/asr-13-37-2016

Cited by 10 publications

(23 citation statements)

References 21 publications

(28 reference statements)

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“…The difference between the blue and red curves (the magenta curve) shows the monthly climate cooling of snow cover with a mean value of ∆T = −10.4 ± 0.4 • C. The standard errors shown are small because of the large number of days in the 49-year record. Other stations show similar plots [15], suggesting that the cold season climatology with and without snow (red and blue curves) are distinct and non-overlapping. Conventionally, they are merged to the black curve, so this can be misleading.…”

Section: Relationship Between Opaque Cloud and Cloud Radiative Forcingmentioning

confidence: 82%

“…The snow depth data begins in 1955, and ends in 1994 for SW, 1997 for MJ, 2002 for LE, 2003 for WI, 2005 for RG and SK, 2006 for ES, GP, MH, PA, RD, RG, and TP, and 2010 for ED. This synthesis paper extracts significant results from many analyses [9][10][11][12][13][14][15][16], which use different subsets of the data, ranging from all station-years with snow depth (e.g., Section 3.1) to selected representative stations, which we will identify in the text. Climate station locations, Canadian ecozones, regional zones, agricultural regions, and boreal forest (adapted from [14]).…”

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confidence: 99%

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Understanding Land–Atmosphere–Climate Coupling from the Canadian Prairie Dataset

Betts

Desjardins

2018

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Analysis of the hourly Canadian Prairie data for the past 60 years has transformed our quantitative understanding of land–atmosphere–cloud coupling. The key reason is that trained observers made hourly estimates of the opaque cloud fraction that obscures the sun, moon, or stars, following the same protocol for 60 years at all stations. These 24 daily estimates of opaque cloud data are of sufficient quality such that they can be calibrated against Baseline Surface Radiation Network data to yield the climatology of the daily short-wave, long-wave, and total cloud forcing (SWCF, LWCF and CF, respectively). This key radiative forcing has not been available previously for climate datasets. Net cloud radiative forcing changes sign from negative in the warm season, to positive in the cold season, when reflective snow reduces the negative SWCF below the positive LWCF. This in turn leads to a large climate discontinuity with snow cover, with a systematic cooling of 10 °C or more with snow cover. In addition, snow cover transforms the coupling between cloud cover and the diurnal range of temperature. In the warm season, maximum temperature increases with decreasing cloud, while minimum temperature barely changes; while in the cold season with snow cover, maximum temperature decreases with decreasing cloud, and minimum temperature decreases even more. In the warm season, the diurnal ranges of temperature, relative humidity, equivalent potential temperature, and the pressure height of the lifting condensation level are all tightly coupled to the opaque cloud cover. Given over 600 station-years of hourly data, we are able to extract, perhaps for the first time, the coupling between the cloud forcing and the warm season imbalance of the diurnal cycle, which changes monotonically from a warming and drying under clear skies to a cooling and moistening under cloudy skies with precipitation. Because we have the daily cloud radiative forcing, which is large, we are able to show that the memory of water storage anomalies, from precipitation and the snowpack, goes back many months. The spring climatology shows the memory of snowfall back through the entire winter, and the memory in summer, goes back to the months of snowmelt. Lagged precipitation anomalies modify the thermodynamic coupling of the diurnal cycle to the cloud forcing, and shift the diurnal cycle of the mixing ratio, which has a double peak. The seasonal extraction of the surface total water storage is a large damping of the interannual variability of precipitation anomalies in the growing season. The large land-use change from summer fallow to intensive cropping, which peaked in the early 1990s, has led to a coupled climate response that has cooled and moistened the growing season, lowering cloud-base, increasing equivalent potential temperature, and increasing precipitation. We show a simplified energy balance of the Prairies during the growing season, and its dependence on reflective cloud.

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Section: Relationship Between Opaque Cloud and Cloud Radiative Forcingmentioning

confidence: 82%

mentioning

confidence: 99%

Understanding Land–Atmosphere–Climate Coupling from the Canadian Prairie Dataset

Betts

Desjardins

2018

Environments

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“…Deer and Grande Prairie, listed in order of increasing latitude (adapted from [12,15]). The line fit…”

Section: Relationship Between Opaque Cloud and Cloud Radiative Forcingmentioning

confidence: 99%

“…We used multiple linear regression to explore the correlation between variables. Following [12,16], our starting format was to regress a standardized thermodynamic anomaly, δY, on opaque cloud anomalies (δOPAQm) for the current month, and lagged precipitation anomalies for the current month (δPR0) and preceding months (δPR1, δPR2, δPR3, δPR4, δPR5) in the form δY = A*δOPAQ + B*δPR0 + C*δPR1 +D*δPR2 +E*δPR3 + F*δPR4 +G*δPR5 (15) Multiple regression shows no memory of cloud for previous months. Since we are using anomalies, the leading coefficient is of order zero, so it is not shown.…”

Section: Hydrometeorology Memory On Monthly Timescalesmentioning

confidence: 99%

“…In April the high albedo of the remaining snowpack, as well as fresh snow, also play a direct climate role, as discussed in section 3.4 and shown in Figure 2, because snow cover acts as a climate Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 8 November 2018 doi:10.20944/preprints201811.0217.v1Peer-reviewed version available at Environments 2018, 5, 129; doi:10.3390/environments5120129 switch. So we computed the standardized April snow cover frequency anomaly, from the fraction of days in April with snow depth >0, and added this to the multiple regression(15) to give δY-Apr = A* δOPAQm-Apr + B*δPR-Apr + C*δPR-Mar + D*δPR-Feb + E*δPR-Jan + F*δPR-Dec + G*δPR-Nov + T*δSnowCover-Apr(16)…”

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confidence: 99%

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Understanding Land-Atmosphere-Climate Coupling from the Canadian Prairie Dataset

Betts¹,

Desjardins²

2018

Preprint

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Analysis of the hourly Canadian Prairie data for the past 60 years has transformed our quantitative understanding of land-atmosphere-cloud coupling. The key reason is that trained observers made hourly estimates of opaque cloud fraction that obscures the sun, moon or stars, following the same protocol for 60 years at all stations. These 24 daily estimates of opaque cloud data are of sufficient quality that they can be calibrated against Baseline Surface Radiation Network data to give the climatology of the daily short-wave, longwave and total cloud forcing (SWCF, LWCF and CF). This key radiative forcing has not been available previously for climate datasets. Net cloud radiative forcing reverses sign from negative in the warm season to positive in the cold season, when reflective snow reduces the negative SWCF below the positive LWCF. This in turn leads to a large climate discontinuity with snow cover, with a systematic cooling of 10°C or more with snow cover. In addition, snow cover transforms the coupling between cloud cover and the diurnal range of temperature. In the warm season, maximum temperature increases with decreasing cloud, while minimum temperature barely changes; while in the cold season with snow cover, maximum temperature decreases with decreasing cloud and minimum temperature decreases even more. In the warm season, the diurnal ranges of temperature, relative humidity, equivalent potential temperature and the pressure height of the lifting condensation level are all tightly coupled to opaque cloud cover. Given over 600 station-years of hourly data, we are able to extract, perhaps for the first time, the coupling between cloud forcing and the warm season imbalance of the diurnal cycle; which changes monotonically from a warming and drying under clear skies to a cooling and moistening under cloudy skies with precipitation. Because we have the daily cloud radiative forci, which is large, we are able to show that the memory of water storage anomalies, from precipitation and the snowpack, goes back many months. The spring climatology shows the memory of snowfall back through the entire winter, and the memory in summer goes back to the months of snowmelt. Lagged precipitation anomalies modify the thermodynamic coupling of the diurnal cycle to the cloud forcing, and shift the diurnal cycle of mixing ratio which has a double peak. The seasonal extraction of the surface total water storage is a large damping of the interannual variability of precipitation anomalies in the growing season. The large land-use change from summer fallow to intensive cropping, which peaked in the early 1990s, has led to a coupled climate response that has cooled and moistened the growing season, lowering cloud-base, increasing equivalent potential temperature, and increasing precipitation. We show a simplified energy balance of the Prairies during the growing season and its dependence on reflective cloud.

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Analysis of near‐surface biases in ERA‐Interim over the Canadian Prairies

Betts

Beljaars

2017

J Adv Model Earth Syst

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We quantify the biases in the diurnal cycle of temperature in ERA‐Interim for both warm and cold season using hourly climate station data for four stations in Saskatchewan from 1979 to 2006. The warm season biases increase as opaque cloud cover decreases, and change substantially from April to October. The bias in mean temperature increases almost monotonically from small negative values in April to small positive values in the fall. Under clear skies, the bias in maximum temperature is of the order of −1°C in June and July, and −2°C in spring and fall; while the bias in minimum temperature increases almost monotonically from +1°C in spring to +2.5°C in October. The bias in the diurnal temperature range falls under clear skies from −2.5°C in spring to −5°C in fall. The cold season biases with surface snow have a different structure. The biases in maximum, mean and minimum temperature with a stable BL reach +1°C, +2.6°C and +3°C respectively in January under clear skies. The cold season bias in diurnal range increases from about −1.8°C in the fall to positive values in March. These diurnal biases in 2 m temperature and their seasonal trends are consistent with a high bias in both the diurnal and seasonal amplitude of the model ground heat flux, and a warm season daytime bias resulting from the model fixed leaf area index. Our results can be used as bias corrections in agricultural modeling that use these reanalysis data, and also as a framework for understanding model biases.

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The impact of clouds, land use and snow cover on climate in the Canadian Prairies

Cited by 10 publications

References 21 publications

Understanding Land–Atmosphere–Climate Coupling from the Canadian Prairie Dataset

Understanding Land–Atmosphere–Climate Coupling from the Canadian Prairie Dataset

Understanding Land-Atmosphere-Climate Coupling from the Canadian Prairie Dataset

Analysis of near‐surface biases in ERA‐Interim over the Canadian Prairies

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