Abstract: This Book Brings Together Expert Opinions From Scientists To Consider The Evidence For Climate Change And Its Impacts On Ticks And Tick-borne Infections, And Provide Predictions For The Future. It Considers What Is Meant By 'Climate Change', How Good Are Climate Models Relevant To Ecosystems, And Predictions For Changes In Climate At Global, Regional, And Local Scales Relevant For Ticks And Tick-borne Infections. It Examines Changes To Tick Distribution And The Evidence That Climate Change Is Responsible. The Effect Of Climate On The Physiology And Metabolism Of Ticks, Including Potentially Critical Impacts On The Tick Microbiome Is Stressed. Given That The Notoriety Of Ticks Derives From Pathogens They Transmit, Do Changes In Climate Affect Vector Capacity? Ticks Transmit A Remarkable Range Of Micro- And Macro-parasites Many Of Which Are Pathogens Of Humans And Domesticated Animals. The Intimacy Between Tick-borne Agent And Tick Vector Means That Any Impacts Of Climate On A Tick Vector Will Impact Tick-borne Pathogens. Most Obviously, Such Impacts Will Be Apparent As Changes In Disease Incidence And Prevalence. The Evidence That Climate Change Is Affecting Diseases Caused By Tick-borne Pathogens Is Considered, Along With The Potential To Make Robust Predictions Of Future Events.

Abstract: This Paper Reports On Consolidated Ground-based Validation Results Of The Atmospheric No2 Data Produced Operationally Since April 2018 By The Tropospheric Monitoring Instrument (Tropomi) On Board Of The Esa/eu Copernicus Sentinel-5 Precursor (S5p) Satellite. Tropospheric, Stratospheric, And Total No2 Column Data From S5p Are Compared To Correlative Measurements Collected From, Respectively, 19 Multi-axis Differential Optical Absorption Spectroscopy (Max-doas), 26 Network For The Detection Of Atmospheric Composition Change (Ndacc) Zenith-scattered-light Doas (Zsl-doas), And 25 Pandonia Global Network (Pgn)/pandora Instruments Distributed Globally. The Validation Methodology Gives Special Care To Minimizing Mismatch Errors Due To Imperfect Spatio-temporal Co-location Of The Satellite And Correlative Data, E.g. By Using Tailored Observation Operators To Account For Differences In Smoothing And In Sampling Of Atmospheric Structures And Variability And Photochemical Modelling To Reduce Diurnal Cycle Effects. Compared To The Ground-based Measurements, S5p Data Show, On Average, (I) A Negative Bias For The Tropospheric Column Data, Of Typically ?23 % To ?37 % In Clean To Slightly Polluted Conditions But Reaching Values As High As ?51 % Over Highly Polluted Areas; (Ii) A Slight Negative Median Difference For The Stratospheric Column Data, Of About ?0.2 Pmolec Cm?2, I.e. Approx. ?2 % In Summer To ?15 % In Winter; And (Iii) A Bias Ranging From Zero To ?50 % For The Total Column Data, Found To Depend On The Amplitude Of The Total No2 Column, With Small To Slightly Positive Bias Values For Columns Below 6 Pmolec Cm?2 And Negative Values Above. The Dispersion Between S5p And Correlative Measurements Contains Mostly Random Components, Which Remain Within Mission Requirements For The Stratospheric Column Data (0.5 Pmolec Cm?2) But Exceed Those For The Tropospheric Column Data (0.7 Pmolec Cm?2). While A Part Of The Biases And Dispersion May Be Due To Representativeness Differences Such As Different Area Averaging And Measurement Times, It Is Known That Errors In The S5p Tropospheric Columns Exist Due To Shortcomings In The (Horizontally Coarse) A Priori Profile Representation In The Tm5-mp Chemical Transport Model Used In The S5p Retrieval And, To A Lesser Extent, To The Treatment Of Cloud Effects And Aerosols. Although Considerable Differences (Up To 2 Pmolec Cm?2 And More) Are Observed At Single Ground-pixel Level, The Near-real-time (Nrti) And Offline (Offl) Versions Of The S5p No2 Operational Data Processor Provide Similar No2 Column Values And Validation Results When Globally Averaged, With The Nrti Values Being On Average 0.79 % Larger Than The Offl Values.

Abstract: Tides are one of the basic types of ocean water motions. Previous studies have reported that the M2 constituent exhibits seasonal variations (annual cycles) in some regions. However, based on the newly proposed method of modified two-step harmonic analysis (HA) and its application at 240 global tide gauges, we find that the M2 constituent as well as the S2 and K1 do not have significant seasonal variations at these tide gauges. The seasonal variations of the M2 constituent reported in previous studies are caused by its satellites, the H1 and H2 constituents, which are not resolved in these studies due to the short time window (one month or three months) used in HA. Because the frequency of the H1 (H2) constituent is equal to that of the M2 minus (plus) the frequency of annual cycles, the superposition of the M2, H1 and H2 constituents with constant amplitudes is equivalent to the M2 constituent with seasonally varying amplitudes. Compared with the new method, some adaptations to traditional HA aiming to capture variations in amplitudes and phase lags of constituents have some limitations, because they either neglect some satellites of the major constitutes or introduce spurious fluctuations resulting from an unreasonably large number of independent points. The nodal modulations of predominant constituents are also explored in this study. On the global scale, the nodal modulations of the M2, K1 and O1 constituents agree with the theoretical predictions, except a cold spot region with reduced nodal modulation in the Gulf of Maine and a hot spot region with enhanced nodal modulation in the South China Sea for the M2. Nodal modulation is also found for the S2 constituent (in theory, the S2 has no nodal modulation), which is 0.8% averaged at 164 tide gauges where the S2 is not too weak.