Detection Of Climate Signals Research Articles

Importance of sampling design to increase climate signal detection in shrub ring chronologies

Shrub dendrochronology is gaining increasing momentum in temperate high mountain regions to decipher climatic controls on current shrub expansion. Yet, a lack of consensus still persists in terms of sampling protocols, thus hampering comparability of results from different studies. For instance, serial sectioning, i.e. the sampling of multiple sections along the same shrub stem is recommended as it increases the detection of partial and missing rings, but has only been employed in few studies as it is time-consuming. Similarly, as a result of serial sectioning, chronologies frequently combine sections sampled at different positions along the stem and at the root collar which hinders the detection of climatic signals. Here, we used cross-sections sampled on 21 Rhododendron ferrugineum shrubs from the French Pyrenees to define a parsimonious protocol enabling detection of partial and missing rings while increasing the strength of the climate signal in the shrub ring chronology. We demonstrate that partial and missing rings are almost evenly distributed along Rhododendron ferrugineum stems and that they can be detected optimally using two sections on which growth rings are measured along three radii. Our results also evidence that chronologies which include only ring-width series from basal sections more strongly integrate summer temperature fluctuations than stem-based or mixed chronologies. Noteworthy, the snowpack signal is stronger in chronologies with individuals from the upper stem sections. Overall, our results confirm that sampling design - serial sectioning and caution in ring-width series aggregation - is key to ensure robustness of dendroecological studies on dwarf shrubs in alpine environments.

On the choice of TLS versus OLS in climate signal detection regression

Total least squares (TLS) or multivariate orthogonal regression is widely used as a remedy for attenuation bias in climate signal detection or “optimal fingerprinting” regression. But under some circumstances it overcorrects and imparts an upward bias, as well as generating extremely unstable and imprecise coefficient estimates. While there has been increasing attention paid recently to the validity of TLS-based confidence intervals, there has been no corresponding examination of coefficient bias problems. This note explains why they are pertinent and presents a Monte Carlo simulation to illustrate the hazards of using TLS in a signal detection application without testing whether the modeling context makes it a suitable choice. TLS is not automatically preferred over OLS even when explanatory variables are believed to contain random errors. Notably it can be sufficiently biased to cause false positives when explanatory signals are negatively correlated, and the bias gets worse as the signal-noise ratio on the explanatory variables rises. Additionally TLS should not be used on its own for climate signal detection inferences since if the no-signal null is true, TLS is generally inconsistent whereas OLS attenuation bias disappears.

From Physics to Climate Science

AbstractI began my career in climate science in 1974 when I went on Sabbatical from a tenured position in a physics department to National Center for Atmospheric Research (NCAR). I began working on simple climate models that year, and it led a few years later to a senior scientist position at NASA Goddard Space Flight Center (1978–1986). During this time, colleagues and I continued working on simple models expanding them to two horizontal dimensions and with many applications in paleoclimatology. When the models were forced by white noise the response was remarkably close to those of general circulation models. My group did numerous studies on the sampling errors in estimating empirical orthogonal functions and in the identification, evaluation, and estimation of errors for satellite retrieval of rain. I was the first study scientist for the Tropical Rainfall Measuring Mission. I moved to Texas A&M University in 1986 and served as department head from 1995–2003. At A&M I continued working on statistical problems related to Tropical Rain Measuring Mission (TRMM) and other applications such as detection of climate signals. I served in many service roles such as on boards, award committees, and as editor‐in‐chief for a journal and an encyclopedia. I also wrote, coauthored, or edited books on paleoclimatology, thermodynamics, climate models, and memoirs. I became Distinguished Professor Emeritus in 2014 and Research Professor until 2020, when I left the payroll.

Comparing precipitation and temperature trends between inland and coastal locations

Motivated by the Millennium Drought and the current drought over much of southern and eastern Australia, this detailed statistical study compares trends in annual wet season precipitation and temperature between a coastal site (Newcastle) and an inland site (Scone). Bootstrap permutation tests reveal Scone precipitation has decreased significantly over the past 40 years (p-value=0.070) whereas Newcastle has recorded little to no change (p-value=0.800). Mean maximum and minimum temperatures for Newcastle have increased over the past 40 years (p-values of 0.002 and 0.015, respectively) while the mean maximum temperature for Scone has increased (p-value = 0.058) and the mean minimum temperature has remained stable. This suggests mean temperatures during the wet season for both locations are increasing. Considering these trends along with those for precipitation, water resources in the Hunter region will be increasingly strained as a result of increased evaporation with either similar or less precipitation falling in the region. Wavelet analysis reveals that both sites have similar power spectra for precipitation and mean maximum temperature with a statistically significant signal in the two to seven year period, typically indicative of the El-Nino Southern Oscillation climate driver. The El-Nino Southern Oscillation also drives the Newcastle mean minimum temperature, whereas the Scone power spectra has no indication of a definitive driver for mean minimum temperature. 
 
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Spectral analysis of climate cycles to predict rainfall induced landslides in the western Mediterranean (Majorca, Spain)

In the present work, spectral analysis has been applied to determine the presence and statistical significance of climate cycles in long-term data series from different rainfall and gauging stations located in the Tramuntana Range, in the north-western sector of the island of Majorca. Climate signals recorded previously in the Mediterranean region have been identified: the ENSO, NAO, HALE, QBO and Sun Spot cycles as well as others related to solar activity; the most powerful signals correspond to the annual cycle, followed by the 6-month and NAO cycles. The incorporation of data derived from gauging stations contributes to better climate signal detection as local and exceptional influences are eliminated. Simulations have been performed for each rainfall/gauging station, using the most significant climate cycles obtained by means of the power spectrum. A good correlation between rainfall/flow values and simulated cycles has been obtained. The NAO and ENSO cycles are the most influential in the rainy periods, and specifically the NAO cycle, where a good correlation between episodes of high rainfall/flow and high values of ANAOI can be observed. At a second stage, landslides dated and recorded in the Tramuntana Range since 1954 (174 events) have been correlated with the simulated cycles obtaining good results, as the landslide events match rainfall peaks well. The correlation for the past decade (since 2005), when a detailed landslide inventory is available, also reveals a coincidence between landslide events and climate cycles, and specifically NAO and ENSO cycles. That is the case of the period 2008–2010, when numerous mass movements took place, and when the largest movement of the inventory was recorded. Results show a potential rainy period in the Tramuntana Range for the coming years (with maximum values around year 2021), when conditions similar to those related to the 2008–2010 event could take place again. The methodology presented in this work can contribute to the prediction of temporal, extreme hydrological events in order to design short-/medium-term mitigation strategies on a regional scale.

Where to see climate change best in radio occultation variables – study using GCMs and ECMWF reanalyses

Abstract. Radio occultation (RO) is a new technique to observe the upper troposphere and lower stratosphere (UTLS), a region that reacts particularly sensitive to climate change. Featuring characteristics such as long-term stability, SI traceability, all-weather capability, global coverage, and high accuracy and vertical resolution, RO data fulfill the requirements for climate monitoring in the UTLS. However, while a range of studies has shown the climate utility of RO it has not yet been explored sytematically where to see climate change best in RO variables. Therefore we perform here a systematic trend study for the RO variables refractivity, pressure, and temperature (bending angle, not depending on height but impact parameter, is left for separate study). The trends, given at geopotential height levels and for layer gradients, are explored to determine seasons, geographic regions, and height domains, which show a significant trend signal. Because continuous RO data are available since 2001 only, reanalyses (ERA-40 and ERA-Interim) and global circulation model simulations of the Intergovernmental Panel on Climate Change Assessment Report 4 (CCSM3, ECHAM5, HadCM3) are used as proxy data for RO. It is shown that RO data are sensitive at different height ranges and that thus several indicators of climate change can be retrieved. Refractivity emerges as indicator in the lower stratosphere (LS) and tropopause region at about 14 km to 24 km, pressure over the whole UTLS, and both in all large-scale regions except the polar caps. Temperature qualifies as indicator in the upper troposphere below about 16 km and in the LS above about 21 km. Overall, refractivity and pressure alone are adequate indicators for the UTLS, but temperature as commonly used variable facilitates easy interpretation of results. Layer gradients were found to be further sensitive indicators providing additional information. Besides large-scale global and hemispheric means the tropics and the mid-latitudes appear as regions suitable to track climate change with RO data. The results also point to the value of utilizing in addition to annual means specific seasons, such as northern hemispheric fall and summer, for early climate signal detection. Since RO data feature much better vertical resolution than the proxy data of this study, more detailed insights can be expected when a longer RO record will be available.

Optimal Detection of Regional Trends Using Global Data

AbstractA complete accounting of model uncertainty in the optimal detection of climate signals requires normalization of the signals produced by climate models; however, there is not yet a well-defined rule for the normalization. This study seeks to discover such a rule. The authors find that, to arrive at the equations of optimal detection from a general application of Bayesian statistics to the problem of climate change, it is necessary to assume that 1) the prior probability density function (PDF) for climate change is separable into independent PDFs for sensitivity and the signals’ spatiotemporal patterns; 2) postfit residuals are due to internal variability and are normally distributed; 3) the prior PDF for sensitivity is uninformative; and 4) a continuum of climate models used to estimate model uncertainty gives a normally distributed PDF for the spatiotemporal patterns for the climate signals. This study also finds that the rule for normalization of the signals’ patterns is a simple division of model-simulated climate change in any observable quantity or set of quantities by a change in a single quantity of interest such as regionally averaged temperature or precipitation. With this normalization, optimal detection yields the most probable estimates of the underlying changes in the region of interest due to external forcings. Data outside the region of interest add information that effectively suppresses the interannual fluctuations associated with internal climate variability.

Climate Signal Detection Times and Constraints on Climate Benchmark Accuracy Requirements

Abstract Long-term trends in the climate system are always partly obscured by naturally occurring interannual variability. All else being equal, the larger the natural variability, the less precisely one can estimate a trend in a time series of data. Measurement uncertainty, though, also obscures long-term trends. The way in which measurement uncertainty and natural interannual variability interact in inhibiting the detection of climate trends using simple linear regression is derived and the manner in which the interaction between the two can be used to formulate accuracy requirements for satellite climate benchmark missions is shown. It is found that measurement uncertainty increases detection times, but only when considered in direct proportion to natural variability. It is also found that detection times depend critically on the correlation time of natural variability and satellite lifetime. As a consequence, requirements on satellite climate benchmark accuracy and mission lifetime must be directly related to the natural variability of the climate system and its associated correlation times.

Detection of climate signal in dendrochronological data analysis: a comparison of tree-ring standardization methods

Tree-ring standardization methods were compared. Traditional methods along with the recently introduced approaches of regional curve standardization (RCS) and power-transformation (PT) were included. The difficulty in removing non-climatic variation (noise) while simultaneously preserving the low-frequency variability in the tree-ring series was emphasized. The potential risk of obtaining inflated index values was analysed by comparing methods to extract tree-ring indices from the standardization curve. The material for the tree-ring series, previously used in several palaeoclimate predictions, came from living and dead wood of high-latitude Scots pine in northernmost Europe. This material provided a useful example of a long composite tree-ring chronology with the typical strengths and weaknesses of such data, particularly in the context of standardization. PT stabilized the heteroscedastic variation in the original tree-ring series more efficiently than any other standardization practice expected to preserve the low-frequency variability. RCS showed great potential in preserving variability in tree-ring series at centennial time scales; however, this method requires a homogeneous sample for reliable signal estimation. It is not recommended to derive indices by subtraction without first stabilizing the variance in the case of series of forest-limit tree-ring data. Index calculation by division did not seem to produce inflated chronology values for the past one and a half centuries of the chronology (where mean sample cambial age is high). On the other hand, potential bias of high RCS chronology values was observed during the period of anomalously low mean sample cambial age. An alternative technique for chronology construction was proposed based on series age decomposition, where indices in the young vigorously behaving part of each series are extracted from the curve by division and in the mature part by subtraction. Because of their specific nature, the dendrochronological data here should not be generalized to all tree-ring records. The examples presented should be used as guidelines for detecting potential sources of bias and as illustrations of the usefulness of tree-ring records as palaeoclimate indicators.

Interannual Temperature Events and Shifts in Global Temperature: A “Multiwavelet” Correlation Approach

For the purpose of climate signal detection, we introduce a method for identifying significant episodes of large-scale oscillatory variability. The method is based on a multivariate wavelet algorithm that identifies coherent patterns of variation simultaneously within particular ranges of time and periodicity (or frequency) that may vary regionally in the timing and amplitude of the particular temperature oscillation. By using this methodology, an analysis is performed of the instrumental record of global temperatures spanning the past 140 years. The duration of an “episode” is chosen to correspond to 3–5 cycles at a specified oscillation period, which is useful for detecting signals associated with the global El Niño/Southern Oscillation (ENSO) phenomenon. To confirm the robustness of signals detected in the earliest, sparse data (only 111 5° longitude by 5° latitude grid points are available back to 1854), we performed multiple analyses overlapping in time, using increasingly dense subsets of the full (1570 grid point) temperature data. In every case, significant interannual episodes are centered in the 3–7 year period range corresponding to the conventional band of ENSO-related variance and describe intervals of quasi-oscillatory variability of decadal-scale duration. These episodes consist of a sequence of one or two warm and cold events with sea surface temperature fluctuations in the eastern tropical Pacific of amplitude ±0.6°–1.1°C. Each episode includes one or more historically prominent El Niño events. The signals are characterized as significant, however, by virtue of their global-scale pattern of temperature variations as well as their oscillatory pattern in time. The 1920–1940 interval of increasing global temperatures was bracketed by oscillatory episodes with unusual global patterns of expression relative to the recent ENSO episodes of the 1970s and 1980s. The episodes that preceded the 1920–1940 and 1975–present intervals of rapid warming were associated with globally averaged temperature fluctuations of TGLB > 0.4°C, the largest among those identified. In contrast, the episode that concludes the 1920–1940 temperature rise exhibits a global-mean fluctuation TGLB = 0.05°C, smallest among the observed episodes. These observations motivate speculation about the possible relationship between ENSO variability and global warming, in particular, the relationship between ENSO and the transient storage of heat in the tropical upper ocean layer, and the relationship between secular climate change and the amplitude of interannual ENSO events.

Optimal climate signal detection in four dimensions

Previous efforts at climate signal detection using the space‐time optimal filter approach have been limited to the time evolution of surface temperature data. This limitation arises from the lack of long‐term data sets of temperature which have sufficient data density in both the horizontal and vertical dimensions of the atmosphere to be useful in such an approach. Future data sets from satellites should provide a wealth of such data. To investigate the potential improvement in climate signal detection when sufficient vertical information becomes available, data from a coupled ocean‐atmosphere climate model is used. For a transient simulation of the doubling of carbon dioxide in the atmosphere, it is found that the signal‐to‐noise ratio is increased by a factor of 2.28 when temperature data from the tropical upper troposphere and stratosphere are used, as compared with using only the tropical surface temperature data. When only tropospheric data are used, the signal‐to‐noise ratio is increased by a factor of 1.12.

Ocean variability, modal group delays, and the detection of climate signals with vertical arrays

The monitoring of ocean variability and climate using global acoustics depends upon detecting travel time changes of the resolvable modes and/or raypaths. An analysis based upon the modal group delays is considered. It is assumed that they contain the information that acoustic probes can yield about an adiabatic ocean; consequently, a water column-spanning vertical line array that measures all group delays can extract all the information about ocean variability and climate that acoustics can provide. The vertical slice is modeled along the path with two components: (i) a set of possible climate signals, e.g., warming at the surface or at the SOFAR duct and (ii) a natural variability based upon the ocean dynamical modes. Noise is included in the measurements of the delays due to acoustic signal-to-noise ratios, finite bandwidths, and array imperfections. Then the covariance of the modal group delays due to natural variability is found and estimation theory bounds are evaluated on how well the climate signals can be estimated using vertical line arrays to measure the group delays. [Work supported by ARPA.]

Detection of Climate Signal by Inter-stake Correlations of Annual Ablation Data Qamanârssûp Sermia, West Greenland

AbstractAnnual ablation on Qamanârssûp sermia, West Greenland, varies with year and location. The time variation of ablation at any stake consists of climate signal and noise due to errors and local topography. Variations of ablation at different stakes are positively correlated with each other with inter-stake correlation coefficients depending on the relative magnitudes of climate signal and noise. Inter-stake correlations and noise are very sensitive to gross errors in the data, while climate signal is less sensitive. Low values of inter-stake correlations are used to detect suspect data, and elimination of such data reduces the noise standard deviation from ±0.40 to ±0.28 m water a−1 for 6 years of record compared with a climate signal of ±0.55 m water a−1. The climate signal on Qamanârssûp is positively correlated with summer mean temperature and negatively correlated with annual precipitation. This shows that even a sparse stake network as at Qamanârssûp sermia gives useful glacier-climate information which can be applied to studies of both glacier hydrology and greenhouse effect.