Trend of monthly temperature and daily extreme temperature during 1951–2012 in New Zealand

A new method for calculating evaporation is proposed, using the Penman–Monteith (P-M) model with remote sensing. This paper achieved the effective estimation to daily evapotranspiration in the Ziya river catchment by using the P-M model based on MODIS remote sensing leaf area index and respectively estimated plant transpiration and soil evaporation by using coefficient of soil evaporation. This model divided catchment into seven different sub-regions which are prairie, meadow, grass, shrub, broad-leaved forest, cultivated vegetation, and coniferous forest through thoroughly considering the vegetation diversity. Furthermore, optimizing and calibrating parameters based on each sub-region and analyzing spatio-temporal variation rules of the model main parameters which are coefficient of soil evaporation f and maximum stomatal conductance g _sx . The results indicate that f and g _sx calibrated by model are basically consistent with measured data and have obvious spatio-temporal distribution characteristics. The monthly average evapotranspiration value of simulation is 37.96 mm/mon which is close to the measured value with 33.66 mm/mon and the relative error of simulation results in each subregion are within 11 %, which illustrates that simulated values and measured values fit well and the precision of model is high. In addition, plant transpiration and soil evaporation account for about 84.64 and 15.36 % respectively in total evapotranspiration, which means the difference between values of them is large. What is more, this model can effectively estimate the green water resources in basin and provide effective technological support for water resources estimation.     

Compilation method for 1 km grid data of monthly mean air temperature for quantitative assessments of climate change impacts

A new method is proposed to compile 1 km grid data of monthly mean air temperature by dynamically downscaling general circulation model (GCM) data with a regional climate model (RCM). The downscaling method used is a technique referred to as the pseudoglobal warming method to reduce GCM bias. For the grid data, RCM data were corrected with data from an existing meteorological network. The correction model for the RCM bias was developed by stepwise multiple regression analysis using the difference in the monthly mean air temperatures between the observation and RCM output as a dependent variable and the geographical factors as independent variables. Our method corrected the RCM bias from 1.69°C to 0.58°C for the month of August in the 1990s (1990–1999).     

Revision and necessary correction of the long-term temperature series of Hohenpeissenberg, 1781–2006

Having been in use at Hohenpeissenberg from 1781–1841, the Palatina thermometer was found to suffer from a positive bias of 0.5°R (or 0.63°C) as discovered by Lamont following a re-calibration made in 1842. The main reason was due to the composition of the glass used during the early instrumental period. Glass of this period tended to contract over many years due to thermal aftereffects, resulting in a gradual rise of the freezing point position in consequence of the shrinking bulb forming the mercury reservoir. While the problem of the gradually rising zero point was recognised relatively early, the reason was attributed to wrong causes. Around 1880, scientists recognised that the chemical composition of glass might be responsible for the drift of the zero point. New glass types were developed which were free from such effects. Although these facts became known, no correction was applied to the Hohenpeissenberg temperature series when in 1981, the complete 200-year series was published. Most probably this bias is also relevant for other stations, at least those of the network of the Societas Meteorologica Palatina that were supplied with thermometers manufactured in Mannheim. Another problem originates from the different observing times for the period 1879–1900, which were set to 0800, 1400 and 2000 hours instead of 0700, 1400 and 2100 hours before and afterwards. In addition, a new formula to calculate the daily mean was established resulting in the temperature being too low by 0.5°C in that period. The overall trend changes after application of the two necessary corrections. There remain two biases that cannot be quantified without a major detailed study being made: (1) At the start of the observations, the window of the observation room was always kept “open during dry weather”. It is not known how long this practice was remained in use. (2) Lamont also employed an easily melting glass to construct his thermometers which in use between 1841 and 1878. An analysis of the glass composition seems to be necessary to find out whether it also suffered from a rising freezing point. Lamont replaced the Hohenpeissenberg thermometer in 1842 by a new instrument produced in his own workshop. One still existing Lamont thermometer, but not that one of Hohenpeissenberg, was re-calibrated and the zero point found to have lowered by ?1.4°C. Since the opposite drift had been expected and the original Lamont-type Hohenpeissenberg thermometer is no longer available, no correction is justified for the period in which this thermometer was in use.     

The method of the medium-range forecast of air temperature in the area of the “Baikonur” cosmodrome

The statistical model of the forecast (complex postprocessing) of surface air temperature with the lead time up to eight days is constructed using the results of the integration of hydrodynamic atmospheric models. The model is adapted to the area of the “Baikonur” using the method of central typing that allows increasing the accuracy of operational forecasts. The analysis of climate characteristics needed for constructing the proper statistical model for this area is given using both observational data for recent 25 years and the data of WMO (from the All-Russian Research Institute of Hydro-meteorological Information-World Data Center). Computed are the estimates of the accuracy of operational forecasts.     

Analysis of temperature variability in the mountain regions of the North Caucasus in 1961–2013

Annual and seasonal series of temperature values are analyzed using the data of Akhty, Teberda, and Terskol weather stations (the height above the sea level is >1000 m) for 1961-2013 as well as from 1976 to 2013 in order to reveal changes in the mountain climate in the period of contemporary global warming. Mean values, standard deviations, norms, and anomalies of annual and seasonal values of temperature as well as the rate of their variation in the mentioned periods are obtained. It is found that the temperature rise is observed in all seasons and for the year as a whole at the mountain weather stations except Terskol station. According to the results of studying temperature variability, Akhty and Teberda weather stations were united into the group “mountain weather stations” with the subsequent averaging of climatic variables. Terskol weather station was singled out as an independent high-mountain weather station.     

Recent anomalies of mean temperature of 12 consecutive months – Germany, Europe, Northern Hemisphere

Anomalies of mean air temperature of 12 consecutive months were detected recently at a number of spatial scales: from local via national and continental, to hemispheric. At all these spatial scales, pre-2007 records were exceeded by considerable margins. The recent rise in the mean temperature of 12 consecutive months at several scales has remained largely unnoticed, because the 12-month means are seldom analyzed. Broad attention is typically focused on monthly, seasonal and annual temperatures. Analysis of shifted 12-month periods helps spot peculiar 12-month episodes with remarkable temperature anomalies that do not coincide with a calendar year. Introducing 12-month running mean temperatures into the global warming debate can prove useful.     

Sensitivity of European glaciers to precipitation and temperature – two case studies

A nonlinear backpropagation network (BPN) has been trained with high-resolution multiproxy reconstructions of temperature and precipitation (input data) and glacier length variations of the Alpine Lower Grindelwald Glacier, Switzerland (output data). The model was then forced with two regional climate scenarios of temperature and precipitation derived from a probabilistic approach: The first scenario (“no change”) assumes no changes in temperature and precipitation for the 2000–2050 period compared to the 1970–2000 mean. In the second scenario (“combined forcing”) linear warming rates of 0.036–0.054°C per year and changing precipitation rates between −17% and +8% compared to the 1970–2000 mean have been used for the 2000–2050 period. In the first case the Lower Grindelwald Glacier shows a continuous retreat until the 2020s when it reaches an equilibrium followed by a minor advance. For the second scenario a strong and continuous retreat of approximately −30 m/year since the 1990s has been modelled. By processing the used climate parameters with a sensitivity analysis based on neural networks we investigate the relative importance of different climate configurations for the Lower Grindelwald Glacier during four well-documented historical advance (1590–1610, 1690–1720, 1760–1780, 1810–1820) and retreat periods (1640–1665, 1780–1810, 1860–1880, 1945–1970). It is shown that different combinations of seasonal temperature and precipitation have led to glacier variations. In a similar manner, we establish the significance of precipitation and temperature for the well-known early eighteenth century advance and the twentieth century retreat of Nigardsbreen, a glacier in western Norway. We show that the maritime Nigardsbreen Glacier is more influenced by winter and/or spring precipitation than the Lower Grindelwald Glacier.     

Cycles and shifts: 1,300 years of multi-decadal temperature variability in the Gulf of Alaska

The Gulf of Alaska (GOA) is highly sensitive to shifts in North Pacific climate variability. Here we present an extended tree-ring record of January–September GOA coastal surface air temperatures using tree-ring width data from coniferous trees growing in the mountain ranges along the GOA. The reconstruction (1514–1999), based on living trees, explains 44% of the temperature variance, although, as the number of chronologies decreases back in time, this value decreases to, and remains around ∼30% before 1840. Verification of the calibrated models is, however, robust. Utilizing sub-fossil wood, we extend the GOA reconstruction back to the early eighth century. The GOA reconstruction correlates significantly (95% CL) with both the Pacific Decadal Oscillation Index (0.53) and North Pacific Index (−0.42) and therefore likely yields important information on past climate variability in the North Pacific region. Intervention analysis on the GOA reconstruction identifies the known twentieth century climate shifts around the 1940s and 1970s, although the mid-1920s shift is only weakly expressed. In the context of the full 1,300 years record, the well studied 1976 shift is not unique. Multi-taper method spectral analysis shows that the spectral properties of the living and sub-fossil data are similar, with both records showing significant (95% CL) spectral peaks at ∼9–11, 13–14 and 18–19 years. Singular spectrum analysis identifies (in order of importance) significant oscillatory modes at 18.7, 50.4, 38.0, 91.8, 24.4, 15.3 and 14.1 years. The amplitude of these modes varies through time. It has been suggested (Minobe in Geophys Res Lett 26:855–858, 1999) that the regime shifts during the twentieth century can be explained by the interaction between pentadecadal (50.4 years) and bidecadal (18.7 years) oscillatory modes. Removal of these two modes of variance from our GOA time series does indeed remove the twentieth century shifts, but many are still identified prior to the twentieth century. Our analysis suggests that climate variability of the GOA is very complex, and that much more work is required to understand the underlying oscillatory behavior that is observed in instrumental and proxy records from the North Pacific region.

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Multiforced statistical assessments of greenhouse-gas-induced surface air temperature change 1890–1985

Based on univariate correlation and coherence analyses and considering the physical basis of the relationships, a simple multiforced (multiple) statistical concept is used which correlates observational climatic time series simultaneously with volcanic, solar, ENSO, and the anthropogenic greenhouse gases forcing. This is appropriate to remove some natural climate noise in the observed data and to evaluate the components (signals) possibly due to the anthropogenic greenhouse gas forcing (CO₂, or equivalent CO₂ implying additional gases) during industrial time. In this paper, we apply this technique to 100 global box data time series 1890–1985, of the surface air temperature, using observed data from Hansen and Lebedeff. The results are presented in terms of latitudinal-seasonal and regional trends, where the observed trend patterns are compared with the hypothetical signals (statistical assessments) possibly due to anthropogenic greenhouse forcing. These latter signals can be amplified to enable a comparison with corresponding results from general circulation model (GCM) CO₂ doubling experiments. These observed-statistical assessments lead to results which are, at least qualitatively and in respect to the zonal mean temperatures, very similar to some GCM experiments indicating the maximum CO₂ doubling signals (statistical assessment > 12 K) in the arctic winter. However, these signals are moderate in the tropics and in the Southern Hemisphere (global average 2.8–4.4 K). As far as the industrial signals are concerned (observed period) these signals are somewhat larger (maximum 7 K, global average 0.5–0.9 K) than the observed trends (maximum 5 K, global average 0.5 K). Phase shifts of cause and effect may amplify these signals but are very uncertain.This paper was presented at the International Conference on Modelling of Global Climate Change and Variability, held in Hamburg 11–15 September 1989 under the auspices of the Meteorological Institute of the University of Hamburg and the Max Planck Institute for Meteorology. Guest Editor for these papers is Dr. L. Dümenil     

The role of ENSO in global ocean temperature changes during 1955–2011 simulated with a 1D climate model

Global average ocean temperature variations to 2,000 m depth during 1955–2011 are simulated with a 40 layer 1D forcing-feedback-mixing model for three forcing cases. The first case uses standard anthropogenic and volcanic external radiative forcings. The second adds non-radiative internal forcing (ocean mixing changes initiated in the top 200 m) proportional to the Multivariate ENSO Index (MEI) to represent an internal mode of natural variability. The third case further adds ENSO-related radiative forcing proportional to MEI as a possible natural cloud forcing mechanism associated with atmospheric circulation changes. The model adjustable parameters are net radiative feedback, effective diffusivities, and internal radiative (e.g., cloud) and non-radiative (ocean mixing) forcing coefficients at adjustable time lags. Model output is compared to Levitus ocean temperature changes in 50 m layers during 1955–2011 to 700 m depth, and to lag regression coefficients between satellite radiative flux variations and sea surface temperature between 2000 and 2010. A net feedback parameter of 1.7Wm^?2 K^?1 with only anthropogenic and volcanic forcings increases to 2.8Wm^?2 K^?1 when all ENSO forcings (which are one-third radiative) are included, along with better agreement between model and observations. The results suggest ENSO can influence multi-decadal temperature trends, and that internal radiative forcing of the climate system affects the diagnosis of feedbacks. Also, the relatively small differences in model ocean warming associated with the three cases suggests that the observed levels of ocean warming since the 1950s is not a very strong constraint on our estimates of climate sensitivity.     

Synoptic drivers of 400 years of summer temperature and precipitation variability on Mt. Olympus,Greece

The Mediterranean region has been identified as a global warming hotspot, where future climate impacts are expected to have significant consequences on societal and ecosystem well-being. To put ongoing trends of summer climate into the context of past natural variability, we reconstructed climate from maximum latewood density (MXD) measurements of Pinus heldreichii (1521–2010) and latewood width (LWW) of Pinus nigra (1617–2010) on Mt. Olympus, Greece. Previous research in the northeastern Mediterranean has primarily focused on inter-annual variability, omitting any low-frequency trends. The present study utilizes methods capable of retaining climatically driven long-term behavior of tree growth. The LWW chronology corresponds closely to early summer moisture variability (May–July, r = 0.65, p < 0.001, 1950–2010), whereas the MXD-chronology relates mainly to late summer warmth (July–September, r = 0.64, p < 0.001; 1899–2010). The chronologies show opposing patterns of decadal variability over the twentieth century (r = ?0.68, p < 0.001) and confirm the importance of the summer North Atlantic Oscillation (sNAO) for summer climate in the northeastern Mediterranean, with positive sNAO phases inducing cold anomalies and enhanced cloudiness and precipitation. The combined reconstructions document the late twentieth—early twenty-first century warming and drying trend, but indicate generally drier early summer and cooler late summer conditions in the period ~1700–1900 CE. Our findings suggest a potential decoupling between twentieth century atmospheric circulation patterns and pre-industrial climate variability. Furthermore, the range of natural climate variability stretches beyond summer moisture availability observed in recent decades and thus lends credibility to the significant drying trends projected for this region in current Earth System Model simulations.     

Spatial and temporal features of summer extreme temperature over China during 1960–2013

Theoretical and Applied Climatology - Based on daily air temperature data from 772 stations in China, the present study uses absolute index and percentile index to investigate the spatial and...     

The use of σ T as a temperature scale in the atmospheric surface layer

The standard deviation of temperature _T is proposed as a temperature scale and as a velocity scale to describe the behaviour of turbulent flows in the Atmospheric Surface Layer (ASL), instead of _* andu _* of the Monin—Obukhov similarity theory, and ofT _f andU _f used for free convection stability conditions. On the basis of experimental evidence reported in the literature, it is shown that _T T _f andv _* U _f in the free convection region, and _{T *} andv _* U _* in nearneutral and stable conditions. This implies that the proposed scales can be applied for all stabilities. Furthermore, a new length scale is proposed and its relation with Obukhov length is given. Also, a simple semi-empirical expression is presented with which _T andv _* can be evaluated in a rather simple way. Some examples of practical applications are given, e.g., a stability classification for unstable conditions.     

Calculating the surface temperature of the solid underlyingsurface by modified “Force-Restore” method

Summary  The calculation of rocky surface temperature encounters the problem of unrealistic results due to its large changes at the interface where the energy balance equation is applied. In order to avoid this, we have modified the “force-restore” method into a self-consistent procedure for simultaneous determination of both surface and the deep ground temperature. The approach is applicable to any celestial body where external radiation can be represented by an arbitrary periodic function. The method is tested with Earth based infrared observation on lunar surface temperature and it showed a high level of accuracy and a rather fast convergence of procedure. Received June 28, 1999/Revised June 19, 2000     

Analysis of the Moberg et al. (2005) hemispheric temperature reconstruction

The Moberg et al. (Nature 433(7026):613–617, 2005. doi:10.1038/nature03265; M05) reconstruction of northern hemisphere temperature variations from proxy data has been criticised; the M05 method may artificially inflate low-frequency variance relative to reality. We test this assertion by undertaking several pseudoproxy experiments in three climate model simulations—one control run and two forced simulations that include several time-varying radiative forcings. The pseudoproxy series are designed to have the same variance spectra as the real M05 proxies, primarily to mimic the low-resolution character of several series. A simple composite-plus-scale (CPS) method is also analysed. In the CPS case all input data behave like annually resolved proxies. The spectral domain performance of both M05 and CPS is found to be dependent on the noise type and noise level in pseudoproxies, on the variance spectrum of the climate model simulation, and on the degree of data smoothing. CPS performs better than M05 in most investigated cases with the control run, but leads to deflated low-frequency variance in some cases. With M05, low-frequency variance tend to be inflated for the control run but not for one of the forced runs and only very slightly with the other forced simulation. Hence, the M05 approach does not routinely inflate low-frequency variance. In our experiment, the M05 approach performs better in the spectral domain than CPS when applied to forced climate model simulations. The results underscore the importance of evaluating the variance spectrum of climate reconstructions.     

Did the global temperature trend change at the end of the 1990s?

The apparent leveling of the global temperature time series at the end of the 1990s may represent a break in the upward trend. A study of the time series measurements for temperature, carbon dioxide, humidity and methane shows changes coincident with phase changes of the Atlantic and Pacific Decadal Oscillations. There are changes in carbon dioxide, humidity and methane measurement series in 2000. If these changes mark a phase change of the Pacific Decadal Oscillation then it might explain the global temperature behavior.