On Spurious Causality, CO2, and Global Temperature

Stips et al. (2016) use information flows (Liang (2008, 2014)) to establish causality from various forcings to global temperature. We show that the formulas being used hinge on a simplifying assumption that is nearly always rejected by the data. We propose the well-known forecast error variance decomposition based on a Vector Autoregression as an adequate measure of information flow, and find that most results in Stips et al. (2016) cannot be corroborated. Then, we discuss which modeling choices (e.g., the choice of CO2 series and assumptions about simultaneous relationships) may help in extracting credible estimates of causal flows and the transient climate response simply by looking at the joint dynamics of two climatic time series.

Combination of Using Pairwise Comparisons and Composite Reference Series: A New Approach in the Homogenization of Climatic Time Series with ACMANT

The removal of non-climatic biases, so-called inhomogeneities, from long climatic records needs sophistically developed statistical methods. One principle is that the differences between a candidate series and its neighbor series are usually analyzed instead of the candidate series directly, in order to neutralize the possible impacts of regionally common natural climate variation on the detection of inhomogeneities. In most homogenization methods, two main kinds of time series comparisons are applied, i.e., composite reference series or pairwise comparisons. In composite reference series, the inhomogeneities of neighbor series are attenuated by averaging the individual series, and the accuracy of homogenization can be improved by the iterative improvement of composite reference series. By contrast, pairwise comparisons have the advantage that coincidental inhomogeneities affecting several station series in a similar way can be identified with higher certainty than with composite reference series. In addition, homogenization with pairwise comparisons tends to facilitate the most accurate regional trend estimations. A new time series comparison method is presented here, which combines the use of pairwise comparisons and composite reference series in a way that their advantages are unified. This time series comparison method is embedded into the Applied Caussinus-Mestre Algorithm for homogenizing Networks of climatic Time series (ACMANT) homogenization method, and tested in large, commonly available monthly temperature test datasets. Further favorable characteristics of ACMANT are also discussed.

Climate Influence in Dendrochronological Series of Araucaria angustifolia from Campos do Jordão, Brazil

A dendrochronological series of Araucaria angustifolia was analyzed for a better understanding of the climatic factors that operate in Campos do Jordão city, São Paulo state, Brazil. The dendroclimatic analysis was carried out using 45 samples from 16 Araucaria angustifolia trees to reconstruct the precipitation and the temperature over the 1803–2012 yearly interval. To this end, Pearson’s correlation was calculated between mean chronology and the climatic time series using a monthly temporal resolution to calibrate our models. We obtained correlations as high as r=0.22(α=0.1) for precipitation (February), and r=0.21(α=0.1) for temperature (March), both corresponding to the end of the summer season. Our results show evidence of temporal instabilities because the correlations for the halves of 1963–2012 were very different, as well as for the full period. To overcome this problem, the dendrochronological series and the climatic data were investigated using the wavelet techniques searching for time-dependent cause–effect relationships. From these analyses, we find a strong influence of the region’s precipitation and temperature on the growth of tree ring widths.

Combination of Using Pairwise Comparisons and Composite Reference Series: A New Approach in the Homogenization of Climatic Time Series

The removal of non-climatic biases, so-called inhomogeneities, from long climatic records needs sophistically developed statistical methods. One principle is that usually the differences between a candidate series and its neighbour series are analysed instead of directly the candidate series, in order to neutralize the possible impacts of regionally common natural climate variation on the detection of inhomogeneities. In most homogenization methods, two main kinds of time series comparisons are applied, i.e. composite reference series or pairwise comparisons. In composite reference series the inhomogeneities of neighbour series are attenuated by averaging the individual series, and the accuracy of homogenization can be improved by the iterative improvement of composite reference series. By contrast, pairwise comparisons have the advantage that coincidental inhomogeneities affecting several station series in a similar way can be identified with higher certainty than with composite reference series. In addition, homogenization with pairwise comparisons tends to facilitate the most accurate regional trend estimations. A new time series comparison method is presented here, which combines the use of pairwise comparisons and composite reference series in a way that their advantages are unified. This time series comparison method is embedded into the ACMANT homogenization method, and tested in large, commonly available monthly temperature test datasets.

Multidimensional extremes: joint study of precipitation and temperature time series

<p>To study climate change, it is essential to analyze extremes as well. The study of extremes can be done on the one hand by examining the time series of extreme climatic events and on the other hand by examining the extremes of climatic time series. In the latter case, if we analyze a single element, the extreme is the maximum or minimum of the given time series. In the present study, we determine the extreme values of climatic time series by examining several meteorological elements together and thus determining the extremes. In general, the main difficulties are connected with the different probability distribution of the variables and the handling of the stochastic connection between them. The first issue can be solved by the standardization procedures, i.e. to transform the variables into standard normal ones. For example, the Standardized Precipitation Index (SPI) uses precipitation sums assuming gamma distribution, or the standardization of temperature series assumes normal distribution. In case of more variables, the problem of stochastic connection can be solved on the basis of the vector norm of the variables defined by their covariance matrix. According to this methodology we have developed a new index in order to examine the precipitation and temperature variables jointly. We present the new index with the mathematical background, furthermore some examples for spatio-temporal examination of these indices using our software MASH (Multiple Analysis of Series for Homogenization; Szentimrey) and MISH (Meteorological Interpolation based on Surface Homogenized Data Basis; Szentimrey, Bihari). For our study, we used the daily average temperature and precipitation time series in Hungary for the period 1870-2020. First of all, our analyses indicate that even though some years may not be considered extreme if only either precipitation or average temperature is taken in to account, but examining the two elements together these years were extreme years indeed. Based on these, therefore, the study of the extremes of multidimensional climate time series complements and thus makes the study of climate change more efficient compared to examining only one-dimensional time series.</p>

The effect of climate change on depression in urban areas of western Iran

Objective Human is accustomed to climatic conditions of the environment where they are born and live throughout their lifetime. The aim of this study is to examine mood swings and depression caused by sudden climate changes that have not yet given the humans a chance to adapt. Results Our results showed that depression could be affected by climate change and as a result, the behavior of climatic elements and trends has damaged mental health in the western regions of Iran. By investigating the trends and changes of climatic time series and their relationship with the rate of depression in urban areas of western Iran, it can be said that climate change is probably a mental health challenge for urban populations. Climate change is an important and worrying issue that makes the life difficult. Rapid climate changes in western Iran including rising air temperature, changes in precipitation, its regime, changes cloudiness and the amount of sunlight have a negative effects on health. The results showed that type of increasing or decreasing trend, as well as different climatic elements in various seasons did not have the same effect on the rate of depression in the studied areas.

Terrestrial hydroclimatic variability in basins of Southern Siberia driven by different states of permafrost degradation

<p>Changes in the cryosphere caused by global warming are expected to alter the hydrologic system, with inevitable consequences for freshwater availability to humans and ecosystems. Quantitative understandings of the historical hydrologic changes in response to permafrost degradation is essential for projecting future changes with respect to the continuing and possibly intensifying warming. Here we investigate past hydro-climatic changes over three southern Siberian basins with diverse permafrost properties: in the Selenga catchment, all three permafrost types occur, i.e., discontinuous, sporadic and isolated permafrost; the Lena Basin (at gauge Tabaga) is mostly underlain by discontinuous permafrost, while the Aldan is dominated by continuous permafrost.</p><p>Based on the reconstruction of terrestrial water storage changes (TWS) from the GRACE satellite mission and hydro-climatic time series over the period 1984-2013, our results show very different change patterns in the TWS among these three basins. There is an unprecedented reduction of TWS (-9.8 km<sup>3</sup>) in the Selenga basin, but remarkable increases (14.4 km<sup>3</sup> and 13.1 km<sup>3</sup>) in the Lena-Tabaga and Aldan basins, respectively. The diverse changes in TWS, runoff and precipitation over each basin suggest different hydrologic response mechanisms to permafrost degradation under a warming climate. The Selenga, dominated by lateral degradation (i.e., decreasing permafrost extent), suffers severe water loss via deep infiltration of water that was previously stored close to the surface, which induces a drier surface and subsurface drainage system. In contrast, in the Aldan basin, determined by vertical degradation, thicker active layers develop which sustain a water-rich surface and subsurface environment. In the Lena-Tabaga basin finally, which is characterized by both lateral and vertical degradations, the further development of lateral degradation has led to a stronger increase in groundwater storage in comparison to surface runoff during the increased precipitation states, suggesting a potentially groundwater-dominated hydrologic system in this basin. Our findings are of great importance for the regional water management in permafrost-affected regions under ongoing warming.</p>

Decline in Temperature Variability on Svalbard

The variability in the temperature on Svalbard, Norway, has been decreasing over the last four decades. This may be due to the reduction in sea ice, transitioning the regional climate to a more stable, coastal one. We quantify this transition in terms of decreasing volatility in a daily average temperature time series at Svalbard Airport from 1976 to 2019. We use two different approaches: a nonstochastic model and a time-dependent generalized autoregressive conditional heteroskedasticity (GARCH) model. These parametric approaches include a time-dependent trend, where the slope depends on the day of the year. For Svalbard, the slope has a minimum in late August and the steepest slope during winter is estimated to be −0.1°C2 yr−1. The nonstochastic model, for which the conditional and unconditional variances are the same, only depends on the marginal distribution and is perhaps the easiest to interpret. The GARCH model extends the nonstochastic model by including short-range temporal dependence in the volatility and is thus more locally adapted. Volatility modeling is important for a complete statistical description of the temperature dynamics on Svalbard as an Arctic representative. In combination with increasing temperatures, the volatility reduction makes the extremely cold days during winter occur less frequently. Although we focus exclusively on the Svalbard Airport series, the models should be suitable for other temperature or climatic time series.