Sensitivity of Bolivian seasonally-dry tropical forest to precipitation and temperature changes over glacial-interglacial timescales

We used fossil pollen to investigate the response of the eastern Chiquitano seasonally-dry tropical forest (SDTF), lowland Bolivia, to high-amplitude climate change associated with glacial–interglacial cycles. Changes in the structure, composition and diversity of the past vegetation are compared with palaeoclimate data previously reconstructed from the same record, and these results shed light on the biogeographic history of today’s highly disjunct blocks of SDTF across South America. We demonstrate that lower glacial temperatures limited tropical forest in the Chiquitanía region, and suggest that SDTF was absent or restricted at latitudes below 17°S, the proposed location of the majority of the hypothesized ‘Pleistocene dry forest arc’ (PDFA). At 19500 yrs b.p., warming supported the establishment of a floristically-distinct SDTF, which showed little change throughout the glacial–Holocene transition, despite a shift to significantly wetter conditions beginning ca. 12500–12200 yrs b.p. Anadenanthera colubrina, a key SDTF taxon, arrived at 10000 yrs b.p., which coincides with the onset of drought associated with an extended dry season. Lasting until 3000 yrs b.p., Holocene drought caused a floristic shift to more drought-tolerant taxa and a reduction in α-diversity (shown by declining palynological richness), but closed-canopy forest was maintained throughout. In contrast to the PDFA, the modern distribution of SDTF most likely represents the greatest spatial coverage of these forests in southern South America since glacial times. We find that temperature is a key climatic control upon the distribution of lowland South American SDTF over glacial-interglacial timescales, and seasonality of rainfall exerts a strong control on their floristic composition.

Influences of increasing temperature on Indian wheat: quantifying limits to predictability

As climate changes, temperatures will play an increasing role in determining crop yield. Both climate model error and lack of constrained physiological thresholds limit the predictability of yield. We used a perturbed-parameter climate model ensemble with two methods of bias-correction as input to a regional-scale wheat simulation model over India to examine future yields. This model configuration accounted for uncertainty in climate, planting date, optimization, temperature-induced changes in development rate and reproduction. It also accounts for lethal temperatures, which have been somewhat neglected to date. Using uncertainty decomposition, we found that fractional uncertainty due to temperature-driven processes in the crop model was on average larger than climate model uncertainty (0.56 versus 0.44), and that the crop model uncertainty is dominated by crop development. Simulations with the raw compared to the bias-corrected climate data did not agree on the impact on future wheat yield, nor its geographical distribution. However the method of bias-correction was not an important source of uncertainty. We conclude that bias-correction of climate model data and improved constraints on especially crop development are critical for robust impact predictions.

Effect of Rht alleles on the tolerance of wheat grain set to high temperature and drought stress during booting and anthesis

Factorial pot experiments were conducted to compare the responses of GA-sensitive and GA-insensitive reduced height (Rht) alleles in wheat for susceptibility to heat and drought stress during booting and anthesis. Grain set (grains/spikelet) of near isogenic lines (NILs) was assessed following three day transfers to controlled environments imposing day temperatures (t) from 20 to 40°C. Transfers were during booting and/or anthesis and pots maintained at field capacity (FC) or had water withheld. Logistic responses (y = c/1+e-b(t -m)) described declining grain set with increasing t, and t5 was that fitted to give a 5% reduction in grain set. Averaged over NIL, t5 for anthesis at FC was 31.7±0.47°C (S.E.M, 26 d.f.). Drought at anthesis reduced t5 by <2°C. Maintaining FC at booting conferred considerable resistance to high temperatures (t5=33.9°C) but booting was particularly heat susceptible without water (t5 =26.5°C). In one background (cv. Mercia), for NILs varying at the Rht-D1 locus, there was progressive reduction in t5 with dwarfing and reduced gibberellic acid (GA) sensitivity (Rht-D1a, tall, 32.7±0.72; Rht-D1b, semi-dwarf, 29.5±0.85; Rht-D1c, severe dwarf, 24.2±0.72). This trend was not evident for the Rht-B1 locus, or for Rht-D1b in an alternative background (Maris Widgeon). The GA-sensitive severe dwarf Rht12 was more heat tolerant (t5=29.4±0.72) than the similarly statured GA-insensitive Rht-D1c. The GA-sensitive, semi-dwarfing Rht8 conferred greater drought tolerance in one experiment. Despite the effects of Rht-D1 alleles in Mercia on stress tolerance, the inconsistency of the effects over background and locus led to the conclusion that semi-dwarfing with GA-insensitivity did not necessarily increase sensitivity to stress at booting and flowering. In comparison to effects of semi-dwarfing alleles, responses to heat stress are much more dramatically affected by water availability and the precise growth stage at which the stress is experienced by the plants.

Equator-to-pole temperature differences and the extra-tropical storm track responses of the CMIP5 climate models

This paper aims to understand the physical processes causing the large spread in the storm track projections of the CMIP5 climate models. In particular, the relationship between the climate change responses of the storm tracks, as measured by the 2–6 day mean sea level pressure variance, and the equator-to-pole temperature differences at upper- and lower-tropospheric levels is investigated. In the southern hemisphere the responses of the upper- and lower-tropospheric temperature differences are correlated across the models and as a result they share similar associations with the storm track responses. There are large regions in which the storm track responses are correlated with the temperature difference responses, and a simple linear regression model based on the temperature differences at either level captures the spatial pattern of the mean storm track response as well explaining between 30 and 60 % of the inter-model variance of the storm track responses. In the northern hemisphere the responses of the two temperature differences are not significantly correlated and their associations with the storm track responses are more complicated. In summer, the responses of the lower-tropospheric temperature differences dominate the inter-model spread of the storm track responses. In winter, the responses of the upper- and lower-temperature differences both play a role. The results suggest that there is potential to reduce the spread in storm track responses by constraining the relative magnitudes of the warming in the tropical and polar regions.

Examining reliability of seasonal to decadal sea surface temperature forecasts: the role of ensemble dispersion

Useful probabilistic climate forecasts on decadal timescales should be reliable (i.e. forecast probabilities match the observed relative frequencies) but this is seldom examined. This paper assesses a necessary condition for reliability, that the ratio of ensemble spread to forecast error being close to one, for seasonal to decadal sea surface temperature retrospective forecasts from the Met Office Decadal Prediction System (DePreSys). Factors which may affect reliability are diagnosed by comparing this spread-error ratio for an initial condition ensemble and two perturbed physics ensembles for initialized and uninitialized predictions. At lead times less than 2 years, the initialized ensembles tend to be under-dispersed, and hence produce overconfident and hence unreliable forecasts. For longer lead times, all three ensembles are predominantly over-dispersed. Such over-dispersion is primarily related to excessive inter-annual variability in the climate model. These findings highlight the need to carefully evaluate simulated variability in seasonal and decadal prediction systems.Useful probabilistic climate forecasts on decadal timescales should be reliable (i.e. forecast probabilities match the observed relative frequencies) but this is seldom examined. This paper assesses a necessary condition for reliability, that the ratio of ensemble spread to forecast error being close to one, for seasonal to decadal sea surface temperature retrospective forecasts from the Met Office Decadal Prediction System (DePreSys). Factors which may affect reliability are diagnosed by comparing this spread-error ratio for an initial condition ensemble and two perturbed physics ensembles for initialized and uninitialized predictions. At lead times less than 2 years, the initialized ensembles tend to be under-dispersed, and hence produce overconfident and hence unreliable forecasts. For longer lead times, all three ensembles are predominantly over-dispersed. Such over-dispersion is primarily related to excessive inter-annual variability in the climate model. These findings highlight the need to carefully evaluate simulated variability in seasonal and decadal prediction systems.

Examining reliability of seasonal to decadal sea surface temperature forecasts: the role of ensemble dispersion

Useful probabilistic climate forecasts on decadal timescales should be reliable (i.e. forecast probabilities match the observed relative frequencies) but this is seldom examined. This paper assesses a necessary condition for reliability, that the ratio of ensemble spread to forecast error being close to one, for seasonal to decadal sea surface temperature retrospective forecasts from the Met Office Decadal Prediction System (DePreSys). Factors which may affect reliability are diagnosed by comparing this spread-error ratio for an initial condition ensemble and two perturbed physics ensembles for initialized and uninitialized predictions. At lead times less than 2 years, the initialized ensembles tend to be under-dispersed, and hence produce overconfident and hence unreliable forecasts. For longer lead times, all three ensembles are predominantly over-dispersed. Such over-dispersion is primarily related to excessive inter-annual variability in the climate model. These findings highlight the need to carefully evaluate simulated variability in seasonal and decadal prediction systems.Useful probabilistic climate forecasts on decadal timescales should be reliable (i.e. forecast probabilities match the observed relative frequencies) but this is seldom examined. This paper assesses a necessary condition for reliability, that the ratio of ensemble spread to forecast error being close to one, for seasonal to decadal sea surface temperature retrospective forecasts from the Met Office Decadal Prediction System (DePreSys). Factors which may affect reliability are diagnosed by comparing this spread-error ratio for an initial condition ensemble and two perturbed physics ensembles for initialized and uninitialized predictions. At lead times less than 2 years, the initialized ensembles tend to be under-dispersed, and hence produce overconfident and hence unreliable forecasts. For longer lead times, all three ensembles are predominantly over-dispersed. Such over-dispersion is primarily related to excessive inter-annual variability in the climate model. These findings highlight the need to carefully evaluate simulated variability in seasonal and decadal prediction systems.

Temporal sensitivity of rice seed development from spikelet fertility to viable mature seed to extreme-temperature

Extreme temperature during reproductive development affects rice (Oryza sativa L.) yield and seed quality. A controlled-environment reciprocal-transfer experiment was designed where plants from two japonica cultivars were grown at 28/24 ⁰C and moved to 18/14 ⁰C and vice versa, or from 28/24 to 38/34 ⁰C and vice versa, for 7-d periods to determine the respective temporal pattern of sensitivity of spikelet fertility, yield, and seed viability to each temperature extreme. Spikelet fertility and seed yield per panicle were severely reduced by extreme temperature in the 14 d period prior to anthesis; and both cultivars were affected at 38/34 ⁰C while only cv. Gleva was affected at 18/14 ºC. The damage was greater the earlier the panicles were stressed within this period. Later-exserted panicles compensated only partly for yield loss. Seed viability was significantly reduced by 7-d exposure to 38/34 ⁰C or 18/14 ⁰C at 1 to 7 and 1 to 14 d after anthesis, respectively, in cv. Gleva. Cultivar Taipei 309 was not affected by 7 d exposure at 18/14 ⁰C; and no consistent temporal pattern of sensitivity was evident at 38/34 ⁰C. Hence, brief exposure to low or high temperature was most damaging to spikelet fertility and yield 14 to 7 d before anthesis, coinciding with microsporogenesis; and it was almost as damaging around anthesis. Seed viability was most vulnerable to low or high temperature in the 7 or 14 d after anthesis, when histodifferentiation occurs.

An investigation into the impact of using various techniques to estimate Arctic surface air temperature anomalies

Time series of global and regional mean Surface Air Temperature (SAT) anomalies are a common metric used to estimate recent climate change. Various techniques can be used to create these time series from meteorological station data. The degree of difference arising from using five different techniques, based on existing temperature anomaly dataset techniques, to estimate Arctic SAT anomalies over land and sea ice were investigated using reanalysis data as a testbed. Techniques which interpolated anomalies were found to result in smaller errors than non-interpolating techniques relative to the reanalysis reference. Kriging techniques provided the smallest errors in estimates of Arctic anomalies and Simple Kriging was often the best kriging method in this study, especially over sea ice. A linear interpolation technique had, on average, Root Mean Square Errors (RMSEs) up to 0.55 K larger than the two kriging techniques tested. Non-interpolating techniques provided the least representative anomaly estimates. Nonetheless, they serve as useful checks for confirming whether estimates from interpolating techniques are reasonable. The interaction of meteorological station coverage with estimation techniques between 1850 and 2011 was simulated using an ensemble dataset comprising repeated individual years (1979-2011). All techniques were found to have larger RMSEs for earlier station coverages. This supports calls for increased data sharing and data rescue, especially in sparsely observed regions such as the Arctic.

Resilience of rice (Oryzaspp.) pollen germination and tube growth to temperature stress

Resilience of rice cropping systems to potential global climate change will partly depend on temperature tolerance of pollen germination (PG) and tube growth (PTG). Germination of pollen of high temperature susceptible Oryza glaberrima Steud. (cv. CG14) and O. sativa L. ssp. indica (cv. IR64) and high temperature tolerant O. sativa ssp. aus (cv. N22), was assessed on a 5.6-45.4°C temperature gradient system. Mean maximum PG was 85% at 27°C with 1488 μm PTG at 25°C. The hypothesis that in each pollen grain, minimum temperature requirements (Tn) and maximum temperature limits (Tx) for germination operate independently was accepted by comparing multiplicative and subtractive probability models. The maximum temperature limit for PG in 50% of grains (Tx(50)) was lowest (29.8°C) in IR64 compared with CG14 (34.3°C) and N22 (35.6°C). Standard deviation (sx) of Tx was also low in IR64 (2.3°C) suggesting that the mechanism of IR64's susceptibility to high temperatures may relate to PG. Optimum germination temperatures and thermal times for 1mm PTG were not linked to tolerating high temperatures at anthesis. However, the parameters Tx(50) and sx in the germination model define new pragmatic criteria for successful and resilient PG, preferable to the more traditional cardinal (maximum and minimum) temperatures.

Thermal remote sensing of sea surface temperature

Sea surface temperature has been an important application of remote sensing from space for three decades. This chapter first describes well-established methods that have delivered valuable routine observations of sea surface temperature for meteorology and oceanography. Increasingly demanding requirements, often related to climate science, have highlighted some limitations of these ap-proaches. Practitioners have had to revisit techniques of estimation, of characterising uncertainty, and of validating observations—and even to reconsider the meaning(s) of “sea surface temperature”. The current understanding of these issues is reviewed, drawing attention to ongoing questions. Lastly, the prospect for thermal remote sens-ing of sea surface temperature over coming years is discussed.

Transition from suppressed to active convection modulated by a weak-temperature gradient derived large-scale circulation

Numerical simulations are performed to assess the influence of the large-scale circulation on the transition from suppressed to active convection. As a model tool, we used a coupled-column model. It consists of two cloud-resolving models which are fully coupled via a large-scale circulation which is derived from the requirement that the instantaneous domain-mean potential temperature profiles of the two columns remain close to each other. This is known as the weak-temperature gradient approach. The simulations of the transition are initialized from coupled-column simulations over non-uniform surface forcing and the transition is forced within the dry column by changing the local and/or remote surface forcings to uniform surface forcing across the columns. As the strength of the circulation is reduced to zero, moisture is recharged into the dry column and a transition to active convection occurs once the column is sufficiently moistened to sustain deep convection. Direct effects of changing surface forcing occur over the first few days only. Afterward, it is the evolution of the large-scale circulation which systematically modulates the transition. Its contributions are approximately equally divided between the heating and moistening effects. A transition time is defined to summarize the evolution from suppressed to active convection. It is the time when the rain rate within the dry column is halfway to the mean value obtained at equilibrium over uniform surface forcing. The transition time is around twice as long for a transition that is forced remotely compared to a transition that is forced locally. Simulations in which both local and remote surface forcings are changed produce intermediate transition times.

Systematic selection of analogue redesign method for forward-type digital power converters

This article proposes a systematic approach to determine the most suitable analogue redesign method to be used for forward-type converters under digital voltage mode control. The focus of the method is to achieve the highest phase margin at the particular switching and crossover frequencies chosen by the designer. It is shown that at high crossover frequencies with respect to switching frequency, controllers designed using backward integration have the largest phase margin; whereas at low crossover frequencies with respect to switching frequency, controllers designed using bilinear integration with pre-warping have the largest phase margins. An algorithm has been developed to determine the frequency of the crossing point where the recommended discretisation method changes. An accurate model of the power stage is used for simulation and experimental results from a Buck converter are collected. The performance of the digital controllers is compared to that of the equivalent analogue controller both in simulation and experiment. Excellent closeness between the simulation and experimental results is presented. This work provides a concrete example to allow academics and engineers to systematically choose a discretisation method.