Surface Temperature Variations in East Africa and Possible Causes

Abstract

Surface temperatures have been observed in East Africa for more than 100 yr, but heretofore have not been subject to a rigorous climate analysis. To pursue this goal monthly averages of maximum (T Max ), minimum (T Min ), and mean (T Mean ) temperatures were obtained for Kenya and Tanzania from several sources. After the data were organized into time series for specific sites (60 in Kenya and 58 in Tanzania), the series were adjusted for break points and merged into individual gridcell squares of 1.258, 2.58, and 5.08.Results for the most data-rich 58 cell, which includes Nairobi, Mount Kilimanjaro, and Mount Kenya, indicate that since 1905, and even recently, the trend of T Max is not significantly different from zero. However, T Min results suggest an accelerating temperature rise.Uncertainty estimates indicate that the trend of the difference time series (T Max 2 T Min ) is significantly less than zero for 1946-2004, the period with the highest density of observations. This trend difference continues in the most recent period , in contrast with findings in recent periods for global datasets, which generally have sparse coverage of East Africa.The differences between T Max and T Min trends, especially recently, may reflect a response to complex changes in the boundary layer dynamics; T Max represents the significantly greater daytime vertical connection to the deep atmosphere, whereas T Min often represents only a shallow layer whose temperature is more dependent on the turbulent state than on the temperature aloft.Because the turbulent state in the stable boundary layer is highly dependent on local land use and perhaps locally produced aerosols, the significant human development of the surface may be responsible for the rising T Min while having little impact on T Max in East Africa. This indicates that time series of T Max and T Min should become separate variables in the study of long-term changes.

What Do Observational Datasets Say about Modeled Tropospheric Temperature Trends since 1979?

“…This occurred during the life of NOAA-12, which developed some calibration problems during its service which may have introduced a small but pervasive warming into all derived products. The impact is small, being about 0.01 to 0.02 °C decade −1 if real [22,23]. Though this evidence from NOAA-12…”

“…It is of interest to note that UAH and RSS have almost identical trends over the extratropics [21]. The shift or drift in RSS temperatures has been documented as a change relative to (a) U.S. NWS radiosonde stations which maintained VIZ instruments, (b) Australian radiosondes, (c) tropical radiosondes, (d) surface datasets and (e) ERA-I reanalyses [6,9,20,22,23]. In addition, this RSS drift has appeared as an unphysical event when compared with vertical ratios of other channels [22,24].…”

“…The likely explanation for at least part of this difference is that the model-based diurnal corrections applied to the satellite data over land, to account for satellite drifting relative to a fixed crossing time, are too large. Additionally, in direct comparison with independent sets of radiosondes (station by station), UAH data produce lower error characteristics than RSS [6,9,22,23]. Since 2002, UAH utilized the microwave radiometer on AQUA, a NASA satellite with on-board propulsion to keep the spacecraft on a consistent crossing schedule, i.e., no drifting through the diurnal cycle.…”

“…Based on that assumption, [26] came to the conclusion that the SRs produced from these datasets carried a fairly large range of uncertainty (±0.95 for T MT /T sfc ). We have three factors working to greatly reduce this error, (1) an observational time series that is 31 years in length, not 21, (2) the use of T LT which avoids the much wider range of error contributions from varying upper troposphere and lower stratosphere uncertainties in the datasets of T MT , and (3) published and displayed information that identifies specific errors in some of the datasets and quantifies errors for the others [6,9,20,[22][23][24]. Thus, using information not available to [26], our results will produce a narrowed range of uncertainty in T LT trends and thus a narrowed range of uncertainty on the value of SR calculated from observations.…”

Performance of the WRF model to simulate the seasonal and interannual variability of hydrometeorological variables in East Africa: a case study for the Tana River basin in Kenya

“…The shortcoming observed in CRU versus station data may be attributed to very few station data records that are assimilated in the global datasets (Christy et al 2009). Furthermore, CRU has a tendency to overestimate temperature in regions with complex topography, which are characterized by an uneven network of stations (e.g., Laux et al 2012).…”